Genetic diagnosis using multiple sequence variant analysis

ABSTRACT

The present invention is in the field of nucleic acid-based genetic analysis. More particularly, it discloses novel insights into the overall structure of genetic variation in all living species. The structure can be revealed with the use of any data set of genetic variants from a particular locus. The invention is useful to define the subset of variations that are most suited as genetic markers to search for correlations with certain phenotypic traits. Additionally, the insights are useful for the development of algorithms and computer programs that convert genotype data into the constituent haplotypes that are laborious and costly to derive in an experimental way. The invention is useful in areas such as (i) genome-wide association studies, (ii) clinical in vitro diagnosis, (iii) plant and animal breeding, (iv) the identification of micro-organisms.

The present application is a continuation-in part of U.S. applicationSer. Nos. 10/788,260 and 10/788,043, now abandoned which were filed onFeb. 26, 2004, and claims the benefit of priority of European PatentApplication No. 03447042.7, which was filed Feb. 27, 2003. Each of theaforementioned applications is incorporated herein by reference in itsentirety.

FIELD OF INVENTION

The present invention is in the field of nucleic acid-based geneticanalysis. More particularly, it discloses novel insights into theoverall structure of genetic variation in all living species.

BACKGROUND OF THE INVENTION

Variation in the human genome sequence is an important determinativefactor in the etiology of many common medical conditions. Heterozygosityin the human population is attributable to common variants of a givengenetic sequence, and those skilled in the art have sought tocomprehensively identify common genetic variations and to link suchvariations to medical conditions [Lander, Science 274:536, 1996; Collinset al., Science 278:1580, 1997; Risch, Science 273:1516, 1996].Recently, it has been estimated that 4 million [Sachidanandam et al.,Nature 409:928 [2001]; Venter et al., Science 291: 1304, 2001] of theestimated 10 million [Kruglyak, Nature Genet 27:234, 2001] common singlenucleotide polymorphisms (SNPs) are already known. These developments inthe field of DNA sequence analysis therefore are providing a rapidaccumulation of partially and completely sequenced genomes. The nextchallenge involves obtaining an inventory of sequence variations(genetic polymorphisms) found in population samples, and using thatinformation to unravel the genetic basis of the phenotypic variationobserved among the individuals of that population. Ideally, suchanalyses would directly reveal the causative genetic variants thatbiochemically determine the phenotype.

In practice, the identification of loci/polymorphisms that haveimportant phenotypic effects involves searching through a large set ofsequence variations to find surrogate markers that are statisticallyassociated with the phenotypic differences through linkagedisequilibrium (LD) with variation(s) (at other sites) that are directlycausative. LD is the non-random association of alleles at adjacentpolymorphisms. When a particular allele at one site, is found to beco-inherited with a specific allele at a second site—more often thanexpected if the sites were segregating independently in thepopulation—the loci are in disequilibrium. LD has recently become thefocus of intense study in the belief that it might offer a shortcut tothe mapping of functionally important loci through whole-genomeassociation studies.

Unfortunately, LD is not a simple function of distance and the patternsof genetic polymorphisms, shaped by the various genomic processes anddemographic events, appear complex. Gene-mapping studies criticallydepend on knowledge of the extent and spatial structure of LD becausethe number of genetic markers should be kept as small as possible sothat such studies can be applied in large cohorts at an affordable cost.Thus, an important analytical challenge is to identify the minimal setof SNPs with maximum total relevant information and to balance anyreduction in the variation that is examined against the potentialreduction in utility/efficiency of the genome-wide survey. Any SNPselection algorithm that is ultimately used should also account for thecost and difficulty of designing an assay for a given SNP on a givenplatform—a particular SNP may be the most informative in a region but itmay also be difficult to measure.

Except for the human species, SNPs have thus far not been surveyedextensively in many other systems. One study [Tenaillon et al., Proc.Natl. Acad. Sci. USA 98: 9161-9166, 2001] investigated the sequencediversity in 21 loci distributed along chromosome 1 of maize (Zea maysssp. mays L.). The sample consisted of 25 individuals representing 16exotic landraces and nine U.S. inbred lines. The first and most apparentconclusion from this study is that maize is very diverse, containing onaverage one SNP every 28 bp in the sample. This is a level of diversityhigher than that of either humans or Drosophila melanogaster. A secondmajor conclusion from the study was that extended regions of high LD maybe uncommon in maize and that genome-wide surveys for associationanalyses in maize require marker densities of one SNP every 100 to 200bp.

Multi-SNP haplotypes have been proposed as more efficient andinformative genetic markers than individual SNPs [Judson et al.,Pharmacogenomics 1: 15-26, 2000; Judson et al;, Pharmacogenomics 3:379-391, 2002; Stephens et al., Science 293: 489-493, 2001; Drysdale etal., Proc. Natl. Acad. Sci. USA 97: 10483-10488, 2000; Johnson et al.,Nat. Genet. 29: 233-237, 2001]. Haplotypes capture the organization ofvariation in the genome and provide a record of a population's genetichistory. Therefore, disequilibrium tests based on haplotypes havegreater power than single markers to track an unobserved, butevolutionary linked, variable site.

Recent studies in human genetics [Daly et al., Nat. Genet. 29: 229-232,2001; Daly et al., patent application US 2003/0170665 A1; Patil et al.,Science 294: 1719-1723, 2001; Gabriel et al., Science 296: 2225-2229,2002; Dawson et al., Nature 418: 544-548, 2002; Philips et al., Nat.Genet. 33: 382-387, 2003; reviewed by Wall & Pritchard, Nature Rev.Genet. 4: 587-597, 2003] have shown that at least part of the genome canbe parsed into blocks: sizeable regions over which there is littleevidence for recombination and within which only a few common haplotypesare observed, i.e. the sequence variants observed in a block oftenappear in the same allelic combinations in the majority of individuals.The major attraction of the ‘haplotype block’ model is that it maysimplify the analysis of genetic variation across a genomic region—theidea is that a limited number of common haplotypes capture most of thegenetic variation across sizeable regions and that these prevalenthaplotypes (and the undiscovered variants contained in these haplotypes)can be diagnosed with the use of a small number of ‘haplotype tag’ SNPs(htSNPs). The ‘haplotype block’ concept has fuelled the InternationalHapMap Project [http://www.hapmap.org; Dennis C., Nature 425: 758-759(2003)]. So far, the haplotype block structure has only beeninvestigated in humans.

Others have reported that a large proportion (75-85%) of the human andDrosophila melanogaster genomes are spanned by so-called “yin-yanghaplotypes”, i.e. a pair of high-frequency haplotypes that arecompletely opposed in that they differ at every SNP [Zhang et al., Am.J. Hum. Genet. 73: 1073-1081, 2003].

Most recently, Carlson and coworkers [Carlson et al., Am. J. Hum. Genet.74: 106-120, 2004] developed an algorithm to select the maximallyinformative subset of SNPs (referred to as tagSNPs) for assay inassociation studies. The selection algorithm is based on the pattern ofLD rather than the ‘haplotype block’ concept. It makes use of the r² LDstatistic to group SNPs as a bin of associated sites. Within the bin anySNP that exceeds an adequately stringent r² threshold with all othersites in the bin may serve as a tagSNP, and only one tagSNP needs to begenotyped per bin. SNPs that do not exceed the threshold with any otherSNP in the region under study are placed in singleton bins.

The determination of haplotypes from diploid unrelated individuals,heterozygous at multiple loci, is difficult. Conventional genotypingtechniques do not permit determination of the phase of several differentmarkers. For example, a genomic region with N bi-allelic SNPs cantheoretically yield 2^(N) haplotypes in the case of completeequilibrium, whereas the actual number should be less than the number ofSNPs in the absence of recombination events and recurrent mutations[Harding et al., Am. J. Hum. Genet. 60: 772-789, 1997; Fullerton et al.,Am. J. Hum. Genet. 67: 881-900, 2000]. Large-scale studies [Stephens etal., Science 293: 489-493, 2001] indicate that the haplotype variationis slightly greater than the number of SNPs.

One approach for determining haplotypes is the use of moleculartechniques to separate the two homologous genomic DNAs. DNA cloning,somatic cell hybrid construction [Douglas et al., Nat. Genet. 28:361-364, 2001], allele-specific PCR [Ruano & Kidd, Nucl. Acids Res. 17:8392, 1989], and single molecule PCR [Ruano et al., Proc. Natl. Acad.Sci. USA 87: 6296-6300, 1990; Ding & Cantor, Proc. Natl. Acad. Sci. USA100: 7449-7453, 2003] have all been used. Alternatively, haplotypes maybe resolved (partially) when the genotypes of first-degree relatives areavailable, e.g. father-mother-offspring trios [Wijsman E. M., Am. J.Hum. Genet. 41: 356-373, 1987; Daly et al., Nat. Genet. 29: 229-232,2001].

To avoid the difficulties and cost in experimental and pedigree-basedapproaches, several computational algorithms have been developed topredict the phase from unrelated individuals or to estimate thepopulation-haplotype frequencies. The approaches include Clark'sparsimony method [Clark A. G., Mol. Biol. Evol. 7: 111-121, 1990],maximum likelihood methods such as the EM algorithm [Excoffier &Slatkin, Mol. Biol. Evol. 12: 921-927, 1995], methods based on Bayesianstatistics such as PHASE [Stephens et al., Am. J. Hum. Genet. 68:978-989, 2001] and HAPLOTYPER [Niu et al., Am. J. Hum. Genet. 52:102-109, 2002], and perfect phylogeny-based methods [Bafna et al. J.Comput. Biol. 10: 323-340, 2003]. These probabilistic methods all havelimitations in accuracy (dependent on the number of SNPs being handledand the size of the population being examined) and scalability.

A number of recent empirical studies [supra] have greatly augmented theknowledge of the overall structure of genetic variation. It should benoted, however, that for example the haplotype block concept remains tobe validated, that not all regions of the human genome may fit theconcept and/or that the concept may have limited value in other species.Irrespective of the outcome, the complexities of genetic variation dataare such that the art would greatly benefit from novel breakthroughsthat advance the understanding of the organization of a population'sgenetic variation, which would eventually lead to theidentification/development of the most informative markers. Discoveriesabout the structure of genetic variations would be useful in differentareas, including (i) genome-wide association studies, (ii) clinicaldiagnosis, (iii) plant and animal breeding, and (iv) the identificationof micro-organisms.

SUMMARY OF THE INVENTION

The present invention discloses novel insights into the overallstructure of genetic variation in all living species. The structure canbe revealed with the use of any data set of genetic variants from aparticular locus. The invention is useful to define the subset ofvariations that are most suited as genetic markers to search forcorrelations with certain phenotypic traits. Additionally, the insightsare useful for the development of algorithms and computer programs thatconvert genotype data into the constituent haplotypes that are laboriousand costly to derive in an experimental way. The invention is useful inareas such as (i) genome-wide association studies, (ii) clinical invitro diagnosis, (iii) plant and animal breeding, (iv) theidentification of micro-organisms.

The present invention is based on the recognition that patterns ofgenetic variation at a locus are formed by clusters of interspersedpolymorphisms that exhibit strong linkage, e.g. the alleles at thepolymorphic sites of each group are essentially found in only twocombinations. These groups of polymorphisms are herein named SequencePolymorphism Clusters (SPC). Certain SPCs are specific to one haplotypewhile others are common to several haplotypes, and thus can be used todefine clades of related haplotypes. The relationship of SPCs can berepresented by means of a hierarchical network. Some SPCs are found inan independent relationship with one another and occur on separatehaplotypes. Other SPCs are dependent and can be ranked according totheir level of inclusiveness: a dependent SPC co-occurs partially withone or more clade-specific SPCs. SPCs can be interrupted byrecombination events. The number of polymorphisms in an SPC as well asits span is variable and, consequently, the set of SPCs in a genomicregion of interest need not share the same boundaries.

A comprehensive catalogue of the SPCs can provide the foundation tosystematically test the involvement of genetic variation in a variety ofphenotypes and traits. The invention relates to methods (computerprograms) of producing (building, making) an SPC map comprising apattern of related SPCs. The SPC map can be used to identify cluster tagpolymorphisms (e.g. ctSNP), which uniquely identify each SPC in an SPCmap of the genomic region of interest for use in subsequent genotypingstudies. An SPC map may depend on the population under study as well ason the size of the sample and should be used accordingly. All or aportion of these ctSNPs can then be used in methods to identify anassociation between a phenotype or trait and an SPC, to localize theposition of a gene associated with the phenotype or trait, to in vitrodiagnose samples for the presence of specific SPC allelic variations,and to determine the identity of samples. The SPC structure can also beused in methods (algorithms, programs) for the deconvolution of diploidgenotypes into the component haplotypes and as a method for theidentification of errors in a collection of genotype calls, which mayrequire experimental verification.

Thus, in one aspect, the invention is directed to an SPC map of a regionof interest of a genome or of an entire genome, comprising a pattern ofrelated SPCs across the region of interest or of the entire genomicregion. In another aspect, the invention is directed to a method ofproducing an SPC map of a region of interest of a genome, comprisingdetermination of the pattern of SPCs across the region of interest. Asdiscussed in further detail below, in one embodiment, the SPC map isproduced starting from haplotypes (sequence or genotyping data). Inanother embodiment, the SPC map is produced starting from unphaseddiploid genotype data. In a still a further alternative embodiment, theSPC map is produced starting from uncharacterized allelic variationdata. In a specific embodiment, the uncharacterized allelic variationdata are obtained by hybridization of the region of interest or theentire genome to arrays of oligonucleotides.

Thus, the present invention is directed to a SPC map of a genomic regionof interest comprising one or more sequence polymorphism clusters(SPCs), wherein each SPC comprises a subset of polymorphisms from thegenomic region wherein the polymorphisms of the subset coincide witheach other polymorphism of the subset. In specific embodiments, eachpolymorphism of the subset coincides with each other polymorphism of thesubset according to a percentage coincidence of the minor alleles of thepolymorphisms of between 75% and 100%. The coincidence of eachpolymorphism with each other polymorphism may be calculated by anyconvenient measure commonly used by those of skill in the art. Inexemplary embodiments, such a calculation may be made according to aparameter selected from but, not limited to, the group consisting of apairwise C value, a r2 linkage disequilibrium value, and a d linkagedisequilibrium value. In particular exemplary embodiments, the parameteris a pairwise C value of from 0.75 to 1.

Also contemplated herein is a method of producing an SPC map of agenomic region of interest comprising the steps of obtaining the nucleicacid sequence of the genomic region of interest from a plurality ofsubjects; identifying a plurality of polymorphisms in the nucleic acidsequences; and identifying one or more SPCs, wherein each SPC comprisesa subset of polymorphisms from the nucleic acid sequence wherein thepolymorphisms of the subset coincide with each other polymorphism of thesubset.

Another specific aspect of the invention contemplates a method ofproducing an SPC map of a genomic region of interest from unphaseddiploid genotypes comprising the steps of obtaining the unphased diploidgenotypes of a genomic region of interest from a plurality of subjects;determining the major and minor metatypes found in the unphased diploidgenotypes; and identifying one or more SPCs, wherein each SPC comprisesa subset of polymorphisms from the metatypes wherein the polymorphismsof the subset coincide with each other polymorphism of the subset.

In the methods of producing the maps of the present invention, it iscontemplated that the identification of the one or more SPCs comprisesidentifying each. polymorphism of the subset that coincides with eachother polymorphism of the subset according to a percentage coincidenceof the minor alleles of the polymorphisms of between 75% and 100%. Inparticular embodiments, it is contemplated that it may, but need notnecessarily, be required to identify the one or more SPCs throughmultiple rounds of coincidence analysis. It may be that in such aniterative process, each successive round of coincidence analysis isperformed at a decreasing percentage coincidence from 100% coincidenceto 75% coincidence. Typically, in the methods the coincidence of eachthe polymorphism of the subset with each other polymorphism of thesubset is calculated according to a parameter selected from the groupconsisting of a pairwise C value, a r2 linkage disequilibrium value, anda d linkage disequilibrium value. In specific embodiments, the parameteris a pairwise C value of from 0.75 to 1.

The polymorphisms identified for use in the producing the SPC maps ofthe invention may be identified using any method conventionally employedto identify polymorphisms and sequence variations. For example, theidentification of a plurality of polymorphisms in the target nucleicacid sequences may be determined by an assay selected from, but notlimited to, the group consisting of direct sequence analysis,differential nucleic acid analysis, sequence based genotyping DNA chipanalysis, and PCR analysis.

A further aspect of the invention includes a method of selecting one ormore polymorphisms from a genomic region of interest for use ingenotyping, comprising the steps of obtaining an SPC map as describedherein, selecting at least one cluster tag polymorphism which identifiesa unique SPC in the SPC map; and selecting a sufficient number ofcluster tag polymorphisms for use in a genotyping study of the genomicregion of interest. In specific embodiments, the cluster tagpolymorphism is selected from the group consisting of a singlenucleotide polymorphism (SNP), a deletion polymorphism, an insertionpolymorphism; and a short tandem repeat polymorphism (STR). Inparticularly preferred embodiments, the cluster tag polymorphism is aknown SNP associated with the trait.

The present invention further provides a teaching of a method ofidentifying a marker for a trait or phenotype comprising obtaining asufficient number of cluster tag polymorphisms as described above; andassessing the cluster tag polymorphisms to identify an associationbetween a trait or phenotype and at least one cluster tag polymorphism,wherein identification of the association identifies the cluster tagpolymorphism as a marker for the trait or phenotype. More particularly,it is preferred that the cluster tag polymorphism is correlated with atrait or phenotype selected from the group comprising a geneticdisorder, a predisposition to a genetic disorder, susceptibility to adisease, an agronomic or livestock performance trait, a product qualitytrait. More specifically, the marker is preferably a marker of a geneticdisorder and the SPC map is prepared by obtaining the nucleic acidsequence of the genomic region of interest from a plurality of subjectsthat each manifests the same genetic disorder; identifying a pluralityof polymorphisms in the nucleic acid sequences; and identifying one ormore SPCs, wherein each SPC comprises a subset of polymorphisms from thenucleic acid sequence wherein the polymorphisms of the subset coincidewith each other polymorphism of the subset. Preferably in these methodsthe identification of a plurality of polymorphisms in the target nucleicacid sequences is determined by an assay selected from the groupconsisting of direct sequence analysis, differential nucleic acidanalysis, sequence based genotyping, DNA chip analysis and polymerasechain reaction analysis.

Also provided herein is a method of identifying the location of a geneassociated with a trait or phenotype comprising identifying a pluralityof SPCs identified in a given genomic region associated with thephenotype, wherein each SPC comprises a subset of polymorphisms from thegenomic region of interest wherein the polymorphisms of the subset areassociated with each other polymorphism of the subset; identifying a setof cluster tag polymorphisms wherein each member of the set of clustertag polymorphisms identifies a unique SPC in said plurality of SPCs; andassessing the set of cluster tag polymorphisms to identify anassociation between a trait or phenotype and at least one cluster tagpolymorphism, wherein identification of the association between thecluster tag polymorphism and the trait or phenotype is indicative of thelocation of the gene. More specifically, the trait or phenotype isselected from the group comprising a genetic disorder, a predispositionto a genetic disorder, susceptibility to a disease, an agronomic orlivestock performance trait, a product quality trait, or any other traitthat may be determined in a genetic analysis.

The present application also contemplates a method for in vitrodiagnosis of a trait or a phenotype in a subject comprising obtaining amarker for the trait or phenotype as outlined above; obtaining a targetnucleic acid sample from the subject; and determining the presence ofthe marker for the trait or a phenotype in the target nucleic acidsample, wherein the presence of the marker in the target nucleic acidindicates that the subject has the trait or the phenotype.

Another aspect of the invention is directed to a method of determiningthe genetic identity of a subject comprising obtaining a reference SPCmap of one or more genomic regions from a plurality of subjects;selecting a sufficient number of cluster tag polymorphisms for thegenomic regions as described herein; obtaining a target nucleic acid ofthe genomic regions from a subject to be identified; determining thegenotype of the cluster tag polymorphisms of the genomic regions of thesubject to be identified; and comparing the genotype of the cluster tagpolymorphism with the SPC to determine the genetic identity of thesubject of interest.

Yet a further embodiment of the present application is directed to amethod method of determining the SPC-haplotypes from unphased diploidgenotype of a genomic region of interest of a subject, comprisingobtaining an SPC map according the methods described herein; determiningthe SPC-haplotypes from said SPC map, wherein each SPC-haplotypecomprises a subset of SPCs from a genomic region wherein said SPCs ofsaid subset coincide; and identifying the SPC-haplotype of a testsubject by comparing the SPCs of said subject with the SPC-haplotypesdetermined from said SPC map.

Yet a further embodiment of the present invention comprises a method ofidentifying an error in a genotype comprising obtaining genotype datafrom a subject of interest and comparing the genotype data with areference SPC map prepared from a plurality of individuals, wherein adifference between the genotype of the subject and the SPC map indicatesan error in the genotype of the subject.

In addition to the methods of the invention, the present inventionfurther contemplates computer programs/algorithms for performing suchmethods. More particularly, the present application describes an articlecomprising a machine-accessible medium having stored thereoninstructions that, when executed by a machine, cause the machine toobtain a nucleic acid sequence information of a genomic region ofinterest from a plurality of subjects; identify a plurality ofpolymorphisms in said nucleic acid sequence; identify one or more SPCs,wherein each SPC comprises a subset of polymorphisms from said nucleicacid sequence wherein said polymorphisms of said subset coincide witheach other polymorphism of said subset. In addition, the article mayhave further instructions that, when executed by the machine, cause themachine to identify each polymorphism of said subset that coincides witheach other polymorphism of said subset according to a percentagecoincidence of the minor alleles of said polymorphisms of between 75%and 100%. The article also may further have instructions that, whenexecuted by the machine, cause the machine to perform each successiveround of coincidence analysis at a decreasing percentage coincidencefrom 100% coincidence to 75% coincidence. Additionally, the article mayhave further instructions that, when executed by the machine, cause themachine to calculate the coincidence of each said polymorphism of saidsubset with each other polymorphism of said subset according to aparameter selected from the group consisting of a pairwise C value, C*value, a r² linkage disequilibrium value, a Δ linkage disequilibriumvalue, a δ linkage disequilibrium value, and a d linkage disequilibriumvalue.

Also part of the instant disclosure is an article comprising amachine-accessible medium having stored thereon instructions that, whenexecuted by a machine, cause the machine to: obtain a set of unphaseddiploid genotypes of a genomic region of interest from a plurality ofsubjects; determine the major and minor metatypes found in said set ofunphased diploid genotypes; identify one or more SPCs, wherein each SPCcomprises a subset of polymorphisms from said metatypes wherein saidpolymorphisms of said subset coincide with each other polymorphism ofsaid subset. This article may further have instructions that, whenexecuted by the machine, cause the machine to identify each polymorphismof said subset that coincides with each other polymorphism of saidsubset according to a percentage coincidence of the minor alleles ofsaid polymorphisms of between 85% and 100%. In addition, the article mayfurther have instructions that, when executed by the machine, cause themachine to identify each polymorphism of said subset that coincides witheach other polymorphism of said subset according to a percentagecoincidence of the minor alleles of said polymorphisms of between 75%and 100%. In addition, the article may have further instructions that,when executed by the machine, cause the machine to identify a pluralityof polymorphisms in said target nucleic acid sequences based on an assayselected from the group consisting of direct sequence analysis,differential nucleic acid analysis, sequence based genotyping DNA chipanalysis, and PCR analysis.

Additionally, the invention provides an article comprising amachine-accessible medium having stored thereon instructions that, whenexecuted by a machine, cause the machine to: obtain an SPC map of agenomic region of interest; select at least one cluster tag polymorphismwhich identifies a unique SPC in the SPC map; and select a sufficientnumber of cluster tag polymorphisms for use in a genotyping study of thegenomic region of interest. Preferably, the article further may havefurther instructions that, when executed by the machine, cause themachine to select the cluster tag polymorphism from the group consistingof a single nucleotide polymorphism (SNP), a deletion polymorphism, aninsertion polymorphism; and a short tandem repeat polymorphism (STR).

Also provided is an article comprising a machine-accessible mediumhaving stored thereon instructions that, when executed by a machine,cause the machine to: obtain a sufficient number of cluster tagpolymorphisms from a genomic region of interest for use in genotyping;assess the cluster tag polymorphisms to identify an association betweena trait or phenotype and at least one cluster tag polymorphism, whereinidentification of the association identifies the cluster tagpolymorphism as a marker for the trait or phenotype. Such an article mayfurther have instructions that, when executed by the machine, cause themachine to correlate a cluster tag polymorphism with a trait orphenotype selected from the group consisting of a genetic disorder, apredisposition to a genetic disorder, susceptibility to a disease, anagronomic or livestock performance trait, a product quality trait. Inaddition, the article may further have instructions that, when executedby the machine, cause the machine to identify the plurality ofpolymorphisms in the target nucleic acid sequences based on an assayselected from the group consisting of direct sequence analysis,differential nucleic acid analysis, sequence based genotyping, DNA chipanalysis and polymerase chain reaction analysis.

Also provided is an article comprising a machine-accessible mediumhaving stored thereon instructions that, when executed by a machine,cause the machine to: identify a plurality of SPCs identified in a givengenomic region associated with a trait or phenotype, wherein each SPCcomprises a subset of polymorphisms from the genomic region wherein thepolymorphisms of the subset are associated with each other polymorphismof the subset; identify a set of cluster tag polymorphisms wherein eachmember of the set of cluster tag polymorphisms identifies a unique SPCin the plurality of SPCs; and assess the set of cluster tagpolymorphisms to identify an association between a trait or phenotypeand at least one cluster tag polymorphism, wherein identification of theassociation between the cluster tag polymorphism and the trait orphenotype is indicative of the location of the gene. Such an article mayhave further instructions that, when executed by the machine, cause themachine to select the trait or phenotype from the group consisting of agenetic disorder, a predisposition to a genetic disorder, susceptibilityto a disease, or an agronomic or livestock performance trait, a productquality trait.

Additionally, the invention teaches an article comprising amachine-accessible medium having stored thereon instructions that, whenexecuted by a machine, cause the machine to: obtain a marker for a traitor phenotype in a subject; obtain a target nucleic acid sample from thesubject; and determine the presence of the marker for the trait or aphenotype in the target nucleic acid sample, wherein the presence of themarker in the target nucleic acid indicates that the subject has thetrait or the phenotype. The article may further have instructions that,when executed by the machine, cause the machine to select the trait orphenotype from the group consisting of a genetic disorder, apredisposition to a genetic disorder, susceptibility to a disease, anagronomic or livestock performance trait, or a product quality trait.

Also provided is an article comprising a machine-accessible mediumhaving stored thereon instructions that, when executed by a machine,cause the machine to: obtain a reference SPC map of one or more genomicregions from a plurality of subjects; select a sufficient number ofcluster tag polymorphisms for the genomic regions; obtain a targetnucleic acid of the genomic regions from a subject to be identified;determine the genotype of the cluster tag polymorphisms of the genomicregions of the subject to be identified; and compare the genotype of thecluster tag polymorphisms with the reference SPC map to determine thegenetic identity of the subject of interest. In addition, there is anarticle comprising a machine-accessible medium having stored thereoninstructions that, when executed by a machine, cause the machine to:obtain an SPC map of a genomic region of interest; determine theSPC-haplotypes from the SPC map, wherein each SPC-haplotype comprises asubset of SPCs from a genomic region wherein the SPCs of the subsetcoincide; and identify the SPC-haplotype of a test subject by comparingthe SPCs of the subject with the SPC-haplotypes determined from the SPCmap.

Other SPC maps of the invention, include an SPC map of a genomic regionof interest comprising one or more sequence polymorphism clusters(SPCs), wherein each SPC comprises a subset of polymorphisms from saidgenomic region wherein said polymorphisms of said subset coincide witheach other polymorphism of said subset; and wherein said map furthercomprises non-clustering polymorphisms that are associated with the map,wherein said non-clustering polymorphisms are such that they do notcluster with any other polymorphism but are associated with at least oneSPC.

Also contemplated is a method of producing an SPC map of a genomicregion of interest comprising the steps of obtaining the nucleic acidsequence of said genomic region of interest from a plurality ofsubjects; identifying a plurality of polymorphisms in said nucleic acidsequences; identifying one or more SPCs, wherein each SPC comprises asubset of polymorphisms from said nucleic acid sequence wherein saidpolymorphisms of said subset coincide with each other polymorphism ofsaid subset; and identifying polymporphisms that do not coincide withany other polymorphism but do cosegregate with at least one SPC.

Another embodiment contemplates a method of producing an SPC map of agenomic region of interest from unphased diploid genotypes comprisingthe steps of obtaining the unphased diploid genotypes of a genomicregion of interest from a plurality of subjects; determining the majorand minor metatypes found in said unphased diploid genotypes;identifying one or more SPCs, wherein each SPC comprises a subset ofpolymorphisms from said metatypes wherein said polymorphisms of saidsubset coincide with each other polymorphism of said subset; andidentifying polymporphisms that do not coincide with any otherpolymorphism but do cosegregate with at least one SPC.

Another method contemplates producing an SPC map of a genomic region ofinterest from the genotypes of sample pools comprising the steps ofobtaining the genotypes of a genomic region of interest from a pluralityof sample pools; determining the major and minor metatypes found in saidgenotypes; identifying one or more SPCs, wherein each SPC comprises asubset of polymorphisms from said metatypes wherein said polymorphismsof said subset coincide with each other polymorphism of said subset.

Also part of the invention is a method of selecting one or morepolymorphisms from a genomic region of interest for use in genotyping,comprising the steps of obtaining an SPC map; selecting at least onecluster tag polymorphism which identifies a specific SPC in said SPCmap; and selecting a sufficient number of cluster tag polymorphisms foruse in a genotyping study of the genomic region of interest.

Yet another method comprises identifying a marker for a trait orphenotype comprising obtaining a sufficient number of cluster tagpolymorphisms; and assessing said cluster tag polymorphisms to identifyan association between a trait or phenotype and at least one cluster tagpolymorphism, wherein identification of said association identifies saidcluster tag polymorphism as a marker for said trait or phenotype.

Also contemplated is a method of in vitro diagnosis of a trait or aphenotype in a subject comprising obtaining a marker for said trait orphenotype; obtaining a target nucleic acid sample from said subject; anddetermining the presence of said marker for said trait or a phenotype insaid target nucleic acid sample, wherein the presence of said marker insaid target nucleic acid indicates that said subject has the trait orthe phenotype.

Another method contemplated is one for the in vitro diagnosis of thepresence of a plurality of genetic variations known to be associatedwith a phenotype or trait in a genomic region of a subject, comprisingthe steps of obtaining an SPC map/network of said genomic region, andselect there from a subset of SPCs, each of which coincides with asubset of the genetic variations; obtaining a target nucleic acid samplefrom said subject; and determining the presence of said subset of SPCsin said target nucleic acid sample, wherein the presence of an SPCidentifies the presence of a subset of genetic variations associatedwith the phenotype or trait in said subject.

A method of determining the genetic identity of a subject is providedwhich comprises obtaining a reference SPC map of one or more genomicregions from a plurality of subjects; selecting a sufficient number ofcluster tag polymorphisms for said genomic regions; obtaining a targetnucleic acid of said genomic regions from a subject to be identified;and determining the genotype of said cluster tag polymorphisms of saidgenomic regions of said subject to be identified; and comparing saidgenotype of said cluster tag polymorphisms with said reference SPC mapto determine the genetic identity of said subject of interest.

Other methods involve determining the SPC-haplotypes from unphaseddiploid genotype of a genomic region of interest of a subject,comprising obtaining an SPC map; determining the SPC-haplotypes fromsaid SPC map, wherein each SPC-haplotype comprises a subset of SPCs froma genomic region wherein said SPCs of said subset coincide; andidentifying the SPC-haplotype of a test subject by comparing the SPCs ofsaid subject with the SPC-haplotypes determined from said SPC map.

Also contemplated is a method of identifying an error in a genotypecomprising obtaining genotype data from a subject of interest andcomparing said genotype data with a reference SPC map prepared from aplurality of individuals, wherein a difference between the genotype ofsaid subject and the SPC map indicates an error in the genotype of saidsubject.

It is contemplated that any of the methods described herein may be usedfor the production of an article that comprises a machine-accessiblemedium having stored thereon instructions that, when executed by amachine, cause the machine to perform the steps of the methods describedabove.

Other features and advantages of the invention will become apparent fromthe following detailed description. It should be understood, however,that the detailed description and the specific examples, whileindicating preferred embodiments of the invention, are given by way ofillustration only, because various changes and modifications within thespirit and scope of the invention will become apparent to those skilledin the art from this detailed description.

DESCRIPTION OF THE DRAWINGS

This patent or application file contains at least one drawing executedin color. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The following drawings form part of the presentspecification and are included to further illustrate aspects of thepresent invention. The invention may be better understood by referenceto the drawings in combination with the detailed description of thespecific embodiments presented herein.

The results shown in FIGS. 1 through 20 that are part of the presentinvention can best be represented and viewed on color printouts. TheFigures are however also legible on black/white printouts where thedifferent colors, referred to in the Figure legends, arerepresented/replaced by different shades of grey or by any other meansof differentially representing or visualizing results. Additionally, theFigures may also incorporate alternative indications (for example anumbering of the originally coloured or shaded regions) to facilitatethe readability of such black/white representations.

FIG. 1 illustrates an SPC structure that consists of a number ofindependent SPCs. An idealized imaginary genetic variation data set,essentially devoid of confounding data, was used. The various SPCs, morespecifically the minor alleles of the SNPs that belong to these SPCs,are differentially highlighted. Different colors are used to indicatethe various SPCs. The representations in FIGS. 1A and 1B correspond tothe output of the algorithm. The first two rows in FIGS. 1A and 1Bindicate respectively the SNPs and the SPCs to which the SNPs belong.FIG. 1A shows the genetic variation table (in which each columnrepresents a polymorphic site and each row represents a sample) ontowhich the SPCs are visualized. The original table is sorted such thatindividuals that share the same SPC are grouped. Polymorphic sites thatdo not cluster are marked in grey (e.g. SNPs 33 and 38). FIG. 1B showsthe matrix of the pairwise C-values calculated from the data set of FIG.1A. All the clustering positions for which C=1 are differentiallyhighlighted and all positions for which C=0 are left blank. The fewpositions where C>0 relate to the limited co-occurrence of SNP-33 andSPC-4. The trivial values on the diagonal do not represent pairwiseassociations but are included in the color scheme to better visualizethe pattern of associated SNPs in the matrix. FIG. 1C shows the SPCnetwork. SPCs are numbered as in FIG. 1A; the putative source sequencethat is devoid of an SPC is referred to as SPC-0.

FIG. 2 illustrates an SPC structure that consists of a number ofdependent SPCs. An idealized imaginary genetic variation data set,devoid of confounding data, was used. Different colors are used toindicate the various SPCs. The representations in FIGS. 2A and 2Bcorrespond to the output of the algorithm. The first two rows in FIGS.2A and 2B indicate respectively the SNPs and the SPCs to which the SNPsbelong. FIG. 2A shows the genetic variation table (in which each columnrepresents a polymorphic site and each row represents a sample) ontowhich the SPCs are depicted. The original table is organized such thatindividuals that share the same SPCs are grouped. Polymorphic sites thatdo not cluster are marked in grey (e.g. SNPs 2, 8, 29, 34 and 38). FIG.2B shows the matrix of the pairwise C-values calculated from the dataset of FIG. 2A. All clustering positions for which C=1 aredifferentially highlighted and all positions for which C=0 are leftblank. The partial co-occurrence of SNPs belonging to dependent SPCs isreflected by pairwise values of C<1. FIG. 2C shows a networkrepresentation of the SPC relationships. SPCs are numbered to reflectthe hierarchy; the putative source sequence that is devoid of an SPC isreferred to as SPC-0. FIGS. 2D and 2E show the SPCs identified in thegenetic variation table and the corresponding networks using a thresholdvalue for C of 0.9. It should be noted that in this case there is nolonger a distinction between SPC-1 and SPC-1.1 of FIG. 2A.

FIG. 3 illustrates a complex SPC structure with both independent anddependent relationships between a total of 12 SPCs. An idealizedimaginary genetic variation data set, essentially devoid of confoundingdata, was used. Different colors are used to indicate the various SPCs.FIG. 3A corresponds to the output of the algorithm and shows the geneticvariation table (in which each column represents a polymorphic site andeach row represents a sample) onto which the SPCs are depicted. Thefirst two rows in FIG. 3A indicate respectively the SNPs and the SPCs towhich the SNPs belong. The original table is sorted such thatindividuals that share the same SPCs are grouped. For the sake ofsimplicity, non-clustering polymorphisms were left out. The networkrepresentation in FIG. 3B shows the hierarchical relationships betweenthe SPCs.

FIG. 4 represents the SPC structure at various stringencies using a dataset containing missing genotype calls. The data set is the same as thatused for FIG. 1 wherein 4.5% of the allele calls were replaced by “N”,symbolizing a missing data point, and 0.5% of the allele calls werereplaced by the opposite allele, to mimic incorrect data. Differentcolors are used to indicate the various SPCs. Throughout the Figure, thesame numbering is used to indicate the various SPCs. FIGS. 4A, 4B and 4Cshow the various SPCs identified at a gradually lower threshold level:C=1, C≧0.9 and C≧0.75 respectively. The first two rows in FIGS. 4A, 4Band 4C indicate respectively the SNPs and the SPCs to which the SNPsbelong. The SNPs that are not clustered are marked in grey while themissing positions (“N”) are left blank. FIG. 4D shows the matrix of thepairwise C values. In this case all positions for which C≧0.75 aredifferentially highlighted and all positions for which C=0 are leftblank. FIG. 4E shows the network structure of the SPCs detected at C=1and C≧0.9, while FIG. 4F shows the network for the SPCs found at C≧0.75.FIGS. 4G, 4H, and 4I illustrate the selection of ctSNPs that tag theSPCs 1, 3 and 4, respectively. For each SPC, a condensed geneticvariation table lists the scores observed at the polymorphic sites thatbelong to that cluster. The accompanying matrix shows the pairwiseC-values as well as a calculation of the average strength of associationof each polymorphism with the other polymorphisms of the cluster. Theseaverage C-values are given along the diagonal as well as in the rightmargin. The most preferred ctSNP is highlighted.

FIG. 5 exemplifies the effect of a limited number of historicalrecombination events on the SPC structure. An imaginary geneticvariation data set was used; non-clustering polymorphisms were omittedfor the sake of simplicity. Different colors are used to indicate thevarious SPCs. Throughout the Figure, the same numbering is used toindicate the various SPCs. FIG. 5A shows the genetic variation tableonto which the SPCs are visualized at a threshold value of C=1. Thefirst two rows in FIG. 5A indicate respectively the SNPs and the SPCs towhich the SNPs belong. The original table was sorted such thatindividuals that share the same SPC are grouped. Certain samples revealrecombination events between SPC-0 and SPC-1. As a result, adjacent setsof SNPs do not cluster perfectly (C=1) and form dependent SPC-1x andSPC-1y. FIG. 5B shows the matrix of the pairwise C-values calculatedfrom the data set of FIG. 5A. All positions for which C=1 aredifferentially highlighted and all positions for which C=0 are leftblank. FIG. 5C shows an SPC map of the locus in question. While SPC-1 isinterrupted on both sides, the other SPCs are continuous. FIG. 5D is anetwork representation of the SPCs detected at C=1. FIGS. 5E and 5F showthe various SPCs found at a threshold level of C≧0.9 and thecorresponding network. FIGS. 5G and 5H show the various SPCs atthreshold level C≧0.8 and the corresponding network.

FIG. 6 exemplifies the effect of a recombination hotspot on the SPCstructure. An imaginary genetic variation data set was used. Differentcolors are used to indicate the various SPCs. The recombination hotspotdemarcates two adjacent regions. A black bar indicates the junction andin the two regions the major alleles (i.e. SPC-0) are differentiallyhighlighted. FIG. 6A shows the original genetic variation table ontowhich the SPCs are depicted. The first two rows in FIG. 6A indicaterespectively the SNPs and the SPCs to which the SNPs belong. The geneticvariation table is arranged such that individuals that share the sameSPCs in the left region are grouped. Polymorphic sites that do notcluster are marked in grey (e.g. SNPs 33, 37 and 38). Note that all SPCsare in an independent relationship and that the SPCs that belong to thedistinct regions occur in various combinations, as indicated in the leftmargin. FIG. 6B shows the matrix of the pairwise C-values calculatedfrom the data set of FIG. 6A. All positions for which C=1 aredifferentially highlighted and all positions for which C=0 are leftblank. Note that in this case the matrix can be spit into twosub-matrices as indicated by the frames. Within each sub-matrix it canbe seen that all SNPs belonging to the same SPC have pairwise values ofC=1, while all SNPs belonging to the different SPCs have pairwise valuesof C=0. Note that the pairwise C-values between the SNPs of region 1 andregion 2 are all <0.5 indicating that there is no clustering between theSPCs of the two regions. FIG. 6C shows an SPC map of the locus inquestion. The SPCs found in the two distinct regions are shownseparately (since they can occur in various combinations). FIG. 6D showsthat each region is characterized by a distinct SPC network.

FIG. 7 illustrates the identification of SPCs that are in an independentconfiguration starting from diploid genotype data as well as thedeconvolution of these genotype data. FIG. 7A is a visual representationof the diploid genotypes, with positions homozygous for the major allelehaving a pale taint, the minor allele having a dark taint and theheterozygous calls (“H”) having a grey taint. The genotype data weregenerated by random pairwise combination of the SPC-haplotypes of FIG.7E. Haplotypes are named according to the SPCs thereby neglecting thenon-clustering SNPs. The haplotype combinations are shown for eachgenotype on the left side. In FIGS. 7B to 7F different colors are usedto indicate the various SPCs. FIG. 7B shows the matrix of the pairwiseC-values calculated from the data set of FIG. 7C. All clustering SNPpositions for which C=1 are differentially highlighted in the same wayas in FIGS. 7C/D/E/F and all positions for which C=0 are left blank.FIGS. 7C and 7D show the metatype table, onto which the SPCs arevisualized, and which for the sake of representation is shown in twohalves. In essence, this table was obtained by duplicating FIG. 7Awherein the “H” positions were replaced once by the minor allele (theresulting minor metatypes are indicated by the letter “a” after thehaplotype combination and are shown in FIG. 7C) and once by the majorallele (the resulting major metatype are indicated by the letter “b”after the haplotype combination and are shown in FIG. 7D). The twotables are sorted such that metatypes that share the same SPC aregrouped as much as possible. Polymorphic sites that do not cluster(positions 33 and 38) are marked in grey. FIG. 7F shows the SPCrelationship which can be deduced from the data in FIGS. 7C and 7D. ThisSPC structure permits the deconvolution of the diploid genotypes intothe component SPC-haplotypes shown in FIG. 7E.

FIG. 8 illustrates the identification of a complex SPC structurestarting from diploid genotype data as well as the deconvolution ofthese data. FIG. 8A is a visual representation of the diploid genotypes,with positions homozygous for the major allele having a pale taint, theminor allele having a dark taint and the heterozygous calls (“H”) havinga grey taint. The genotype data were generated by random pairwisecombination of the SPC-haplotypes in FIG. 8E. In case the combinedalleles were different, these were replaced by “H”. The haplotypecombinations are shown for each genotype on the left side. In FIGS. 8Bto 8F different colors are used to indicate the various SPCs. FIG. 8Bshows the matrix of the pairwise C-values calculated from the data setof FIG. 8C. All clustering SNP positions for which C=1 aredifferentially highlighted in the same way as in FIG. 8C/D/E/F and allpositions for which C=0 are left blank. FIGS. 8C and 7D show themetatype table, onto which the SPCs are visualized, and which for thesake of representation is shown in two halves. In essence, this tablewas obtained by duplicating FIG. 8A wherein the “H” positions werereplaced once by the minor allele (the resulting minor metatypes areindicated by the letter “a” after the haplotype combination and areshown in FIG. 8C) and once by the major allele (the resulting majormetatype are indicated by the letter “b” after the haplotype combinationand are shown in FIG. 8D). The two tables are sorted such that metatypesthat share the same SPC are grouped as much as possible. FIG. 8F showsthe SPC relationship which can be deduced from the data in FIG. 8C. ThisSPC structure permits the deconvolution of the diploid genotypes intothe component SPC-haplotypes shown in FIG. 8E.

FIG. 9 shows the intraspecies SPC map of the sh2 locus of maize.Different colors are used to indicate the various SPCs. FIG. 9Acorresponds to the output of the algorithm and shows the geneticvariation table onto which the SPCs are depicted. The maize lines foreach genotype are shown in the left most column. The position of eachvariation on the physical map of the 7 kb sh2 locus is indicated abovethe columns. The polymorphic sites in the middle segment of the locusare omitted to bring down the size of the table. The table is organizedsuch that individuals that share the same SPCs are grouped. Polymorphicsites that do not cluster are for the most part omitted—the ones thatare shown are colored in grey and are located at positions 924, 936,1834, 1907 and 1971. FIG. 9B shows the SPC network of the locus. Theputative source sequence that is devoid of an SPC is referred to asSPC-0.

FIG. 10 shows the intraspecies SPC map of the sh1 locus of maize.Different highlights are used to indicate the various SPCs. The upperpart of the figure is a schematic representation of the physical map ofthe 7 kb sh1 locus, in which the differentially highlighted rectanglesindicate the map positions of the polymorphic sites that are listed inthe genetic variation table. The middle panel corresponds to the outputof the algorithm and lists the different SPCs in the locus. Each rowrepresents the polymorphic sites that belong to a particular SPC. Thelower panel corresponds to the output of the algorithm and shows thegenetic variation table onto which the SPCs are depicted. The maizelines for each genotype are shown in the left most column. The table isorganized such that individuals that share the same SPCs are grouped asmuch as possible. Polymorphic sites that do not cluster are not shown.

FIG. 11 shows the intraspecies SPC map of the Y1 locus of maize.Different colors are used to indicate the various SPCs. FIG. 11A is aschematic representation of the physical map of the 6 kb Y1 locus, inwhich the differentially highlighted rectangles indicate the mappositions of the polymorphic sites that are listed in the geneticvariation table of FIG. 11B. FIG. 11B corresponds to the output of thealgorithm and shows the genetic variation table onto which the SPCs aredepicted. The maize lines for each genotype are shown in the left mostcolumn. The upper panel of FIG. 11B shows the SPCs in the whiteendosperm lines. The lower panel of FIG. 11B shows the SPCs in theorange/yellow endosperm lines. The table is organized such thatindividuals that share the same SPCs are grouped as much as possible.The arrows indicate the positions of some putative historicalrecombination events. Polymorphic sites that do not cluster are notshown.

FIG. 12 shows the interspecies SPC map of the globulin 1 locus of maize.Different colors are used to indicate the various SPCs. Therepresentation in FIG. 12A corresponds to the output of the algorithmand shows the genetic variation table onto which the SPCs are depicted.Non-clustering polymorphisms and some SPCs that cannot be placed in thenetwork structure were omitted. The abbreviated species and accessionnumbers for each genotype are shown in the second column. The table isorganized such that individuals that share the same independent SPC aregrouped as indicated by the differentially highlighted left most column.The arrows indicate the Zea mays accessions that share SPCs with Zeaperennis. FIG. 12B shows the SPC network and the Zea species. Theatypical branching of SPCs 1 and 3 symbolizes that both these SPCs shareone polymorphism with SPC-2. The putative source sequence that is devoidof an SPC is referred to as SPC-0.

FIG. 13 shows the SPC map of the FRI locus of Arabidopsis thaliana.Different colors are used to indicate the various SPCs. FIG. 13A is aschematic representation of the physical map of the 450 kb FRI locus, inwhich the differentially highlighted rectangles symbolize the sequencedregions and also indicate the map positions of the polymorphic sitesthat are listed in the genetic variation table of FIG. 13B. FIG. 13Bcorresponds to the output of the algorithm and shows the geneticvariation table onto which the SPCs are depicted. The Arabidopsis linesfor each genotype are shown in the left most column. The table isorganized such that individuals that share the same SPCs are grouped asmuch as possible.

FIG. 14 shows the SPC maps of 31 amplicons from a 3.76 Mb segment ofchromosome 1 of Arabidopsis thaliana. Different colors are used toindicate the various SPCs. The figure is composed of 6 panels, numbered1 through 6, which represent 100 polymorphic sites each. The rectanglesat the top of each panel represent the amplicons from which thepolymorphic sites were analyzed. The amplicons are numbered from 134through 165, corresponding respectively to positions 16,157,725 and19,926,385 on chromosome 1. Note that the missing amplicon 149 has nopolymorphic sites. The dotted lines that divide the panels mark theboundaries of the blocks of polymorphisms that belong to each amplicon.Each SPC is represented on a different row and marked by a differentcolor. SPCs that span adjacent amplicons are outlined and marked byblack arrows. The empty blocks represent the amplicons that have noSPCs. Note that amplicons may be represented in consecutive panels, andthat corresponding SPCs may be represented on different rows and markedby a different color.

FIG. 15 shows the SPC structure of the human CYP4A11 gene. Differentcolors are used to indicate the various SPCs. FIG. 15A corresponds tothe output of the algorithm and shows the metatype table onto which theSPCs are depicted. The sample names for each metatype are shown in theleft most column, and are denoted with the extension “−1” for the minormetatype and the extension “−2” for the major metatype. The position ofeach polymorphic site in the sequence of the CYP4A11 gene is indicatedabove the columns. Polymorphic sites that do not cluster are omitted.The table is organized such that metatypes that share the same SPCs aregrouped. The upper panel shows the major metatypes and the lower panelthe minor metatypes. Metatypes that have no SPCs are omitted except forone in each panel. In the upper row the polymorphic sites are numberedconsecutively and the sites that were clustered at the threshold of C=1are highlighted. FIG. 15B shows the different SPC combinations observedin the three classes of metatypes. Each rectangle of two rows shows theminor and the major metatype of a sample, the SPCs observed and the SPCcombinations. The two SPC-haplotypes are obtained after deconvolution ofthe genotype. FIG. 15C presents the hierarchical relationship betweenthe SPCs of the CYP4A11 gene. The putative source sequence that isdevoid of an SPC is referred to as SPC-0. The full and dotted linesrepresent respectively confirmed and putative relationships. FIG. 15Dshows the SPC map of the CYP4A11 gene. The upper panel shows theinferred SPC-haplotypes onto which the SPCs are depicted. The lowerpanel represents the SPCs such that each SPC is represented on adifferent row and marked by a different color. FIGS. 15E, F and Gillustrate the selection of ctSNPs that tag the SPCs 1, 2 and 4,respectively. For each SPC, a condensed metatype table lists the scoresobserved at the polymorphic sites that belong to that cluster. Theaccompanying matrix shows the pairwise C-values as well as a calculationof the average strength of association of each polymorphism with theother polymorphisms of the cluster. These average C-values are givenalong the diagonal as well as in the right margin. The most preferredctSNPs are highlighted.

FIG. 16 shows the SPC structure of a segment of the human MHC locus.Different colors are used to indicate the various SPCs. FIG. 16A is aschematic representation of the physical map of the 200 kb Class IIregion of the MHC locus, in which the differentially highlightedrectangles symbolize the 7 domains from FIGS. 16B and C. The positionsof the hotspots of recombination are indicated by the vertical arrows.FIGS. 16B and C show the SPC map of the region in which each SPC isrepresented on a different row and marked by a different color. Thedifferentially highlighted rectangles represent the domains inferredfrom the SPC maps. FIG. 16B represents the SPC map of the subgroup ofSNPs with high frequency minor alleles (frequency >16%) and FIG. 16Crepresents the SPC map of the subgroup the SNPs characterized by lowfrequency minor alleles (≦−16%). SPCs that span different domains areoutlined and marked by horizontal arrows. FIG. 16D shows an SPC map ofdomain 4 of FIG. 16A from position 35,095 to position 89,298. In theupper row the polymorphic sites are numbered consecutively and thephysical map position of each polymorphic site is indicated above thecolumns. Polymorphic sites that do not cluster are omitted. The upperpanel shows the inferred SPC-haplotypes onto which the SPCs aredepicted. The lower panel shows the SPCs in which each SPC isrepresented on a different row and marked by a different color. FIG. 16Epresents the hierarchical relationship between the SPCs of domain 4.

FIG. 17 shows the SPC map of the HapMap SNPs of human Chromosome 22.FIG. 17A is a schematic representation of the physical map of a segmentof 2.27 Mb of chromosome 22 in which the differentially highlighted andnumbered rectangles symbolize the 11 domains of FIG. 17B. The domainsare drawn to scale. The map positions represent the positions onchromosome 22. FIG. 17B shows the SPC map of 700 SNPs of chromosome 22.The figure is composed of 7 panels, numbered 1 through 7, whichrepresent 100 polymorphic sites each. The rectangles at the top of eachpanel represent the domains comprising 10 or more clustered SNPs. Allnon overlapping SPCs are shown on the first row of each panel, whileoverlapping SPCs are displayed in consecutive rows. Different colors areused to mark the different SPCs. Note that domains may be represented inconsecutive panels, and that corresponding SPCs may be represented ondifferent rows and marked by a different color. FIG. 17C shows the SPCmap of domain 9 of FIG. 17B from position 17,399,935 to position17,400,240. The chromosomal map position of each SNP is indicated abovethe columns. The figure shows the inferred SPC-haplotypes onto which theSPCs are depicted. Polymorphic sites that do not cluster are omitted.FIG. 17D presents the hierarchical relationship between the SPCs ofdomain 9. It can be seen that one of the haplotypes, 6-1-2-3-5, has acomplex history. FIG. 17E corresponds to the output of the algorithm andshows the metatypes of three trios (parents and child) onto which theSPCs are depicted, with their corresponding SPC-haplotypes. Themetatypes are shown in the order: parents (father and mother; marked P)and child (marked C). The alleles marked by a black frame and arrowsrepresent the genotyping errors.

FIG. 18 shows the SPC map of 500 kilobases on chromosome 5q31. Differentcolors are used to indicate the various SPCs which are represented ondifferent rows. SNPs that do not cluster are shown on the bottom row.The SNP names are indicated above the columns. The grey rectangles,numbered 1 through 11, represent the haplotype blocks identified by Dalyet al. [Daly et al., Nat. Genet. 29: 229-232, 2001]. SPCs than spandifferent haplotype blocks are framed in their respective colors.

FIG. 19 shows the SPC map of single-feature polymorphisms (SFPs) inyeast. Different colors are used to indicate the various SPCs. The upperpanel shows the SPCs in which each SPC is represented on a different rowand marked by a different color. The lower panel corresponds to theoutput of the algorithm and shows the genetic variation table onto whichthe SPCs are depicted. Only those SFPs that belong to SPCs having 4 ormore SFPs are shown. The yeast strains for each genotype are shown inthe left most column. The position of each variation on the physical mapof chromosome 1 is indicated above the columns.

FIG. 20 shows the SPC map of the glnA locus of Campylobacter jejuni.Different colors are used to indicate the various SPCs. The upper panelshows the SPCs in which each SPC is represented on a different row andmarked by a different color. The lower panel corresponds to the outputof the algorithm and shows the genetic variation table onto which theSPCs are depicted. Only those polymorphisms that belong to SPCs having 3or more polymorphisms are shown. The Campylobacter jejuni strains foreach genotype are shown in the left most column. The position of eachvariation is indicated above the columns.

FIG. 21 is a schematic diagram of some of the components of a computer.

FIG. 22 is an exemplary flowchart showing some of the steps used tofacilitate the production of an SPC map of a genomic region of interest.

FIG. 23 is an exemplary flowchart showing some of the steps used in analternative embodiment to the embodiment shown in FIG. 22.

FIG. 24 is an exemplary flowchart showing some of the steps used in amethod of selecting one or more polymorphisms from a genomic region ofinterest for use in genotyping.

FIG. 25 is an exemplary flow chart describing some of the steps used tofacilitate the identification of a marker trait or phenotype.

FIG. 26 is an exemplary flow chart describing some of the steps used tofacilitate the identification of a location of a gene associated with atrait or phenotype.

FIG. 27 is an exemplary flow chart describing some of the steps used ina method for in vitro diagnosis of a trait or phenotype.

FIG. 28 is an exemplary flow chart describing some of the steps used ina method of determining the genetic identity of a subject.

FIG. 29 is an exemplary flow chart describing some of the steps used ina method of determining the SPC-haplotypes from unphased diploidgenotype of a genomic region of interest.

FIG. 30 illustrates the rooting of an SPC network by means of anoutspecies sequence. The region under study runs from position126,499,999 to 126,612,618 on human chromosome 7 (build 34). Panel Ashows the genetic variation data set onto which the SPCs are depicted.Each row represents a sample and each column symbolizes an SNP. Theallelic state is represented by colors: minor alleles are coloredaccording to the SPC they belong to while the major allele is indicatedby a light yellow color. The table is organized such that individualsthat share the same SPCs are grouped. The horizontal lines and thenumbering to the left indicate the SPCs and the major haplotypes. PanelB shows the SPC network. In contrast to the standard representationsherein, the present network indicates, for each SPC, the number of SNPs(also reflected by the size of the nodes) as well as the occurrencefrequency. Panel C shows the table of genetic variations relative to abona fide ancestral sequence (compare with the table shown in panel A).Part of the SPC-1 minor SNP alleles turned out to be ancestral. As aconsequence, the major allele is colored at these polymorphic sites.Panel D shows the rooted SPC network. SPC-1 (see panel B) is split intoSPC-1M (polymorphic sites where the major allele corresponds to thechimpanzee sequence) and SPC-1m (sites where the minor allele isancestral).

FIG. 31A illustrates the effect of SPC frequency and pool size on thesuccess rate of identification of a series of independent SPCs using apooling strategy. FIG. 31B illustrates the same for SPCs that are in adependent relationship. The genotypes of sample pools were generated byrandom combination of known haplotypes and were subsequently analyzed bythe SPC algorithm. The figure plots the success rate with whichparticular SPCs were identified in 100 repeat analyses using variouspool sizes.

FIG. 32 shows an SPC network that includes non-clustering polymorphisms.The region under study runs from position 126,135,436 to 126,178,670 onhuman chromosome 7 (build 34). Panel A shows the genetic variation dataset onto which the SPCs as well as the non-clustering SNPs are depicted.Each row represents a sample and each column symbolizes an SNP. Theallelic state is represented by colors: minor alleles are coloredaccording to the SPC they belong to while the major allele is indicatedby a light yellow color. The table is organized such that individualsthat share the same SPCs/non-clustering SNP are grouped. The horizontallines and the numbering to the left indicate the SPCs and the majorhaplotypes. Panel B shows the SPC network. For each SPC, the number ofSNPs (also reflected by the size of the nodes) as well as the occurrencefrequency is indicated. Panel C represents the SPC network to which thenon-clustering SNPs were added (symbolized by the digit 1).

FIG. 33 illustrates the unambiguous placement of non-clusteringpolymorphisms in the SPC network of various Arabidopsis genomic regions.Each panel (A, B, C, D, and E) shows the SPC structure in one of fiveamplicons derived from Arabidopsis chromosome 1. All polymorphisms,including the singletons and those that do not cluster, wereincorporated. The genetic variation tables contain the scores at thevarious polymorphic sites (columns) for a multitude of samples (rows).As explained in the text, tri-allelic SNPs and indels of two or morenucleotides are converted into two polymorphic scores. The allelic stateis represented by colors: minor alleles are colored according to the SPCthey belong to while the major allele is indicated by a light yellowcolor. The table is organized such that individuals that share the sameSPCs are grouped. The horizontal lines separate the various SPCs/majorhaplotypes. The red arrowheads above the table indicate the polymorphicscores (colored in gray) that do not conform to the SPC network. Inpanel A, B and D, the arrows indicate the (presumably erroneous) allelecalls that cause the nonconformity. In contrast to the standardrepresentations herein, the present networks indicate, for each SPC, thenumber of SNPs (also reflected by the size of the nodes) as well as theoccurrence frequency.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to methods, algorithms and computerprograms for revealing the structure of genetic variation and to theselection of the most informative markers on the basis of the underlyingstructure. The methods can be applied on any data set of geneticvariation from a particular locus. In one aspect, the analysis of thegenetic variation is based on haplotype data. In a second aspect, thestructure is uncovered using diploid genotype data, thereby avoiding theneed to either experimentally or computationally infer the componenthaplotypes. In a third aspect, the present method can be applied ontouncharacterized allelic variation that results from the interrogation ofa target nucleic acid with an experimental procedure that provides arecord of the sequence variation present but does not actually providethe entire sequence or, in particular, the sequence at the variablepositions. The underlying structure of genetic variation is also usefulfor the deduction of the constituent haplotypes from diploid genotypedata.

The term “polymorphism”, as used herein, refers to a condition in whichtwo or more different nucleotide sequences can exist at a particularlocus in DNA. Polymorphisms can serve as genetic markers. Polymorphismsinclude “single nucleotide polymorphism” (SNP) and indels. Suchpolymorphisms also are known as restriction fragment lengthpolymorphisms (RFLP). A RFLP is a variation in DNA sequence that altersthe length of a restriction fragment, as described in Botstein et al.,Am. J. Hum. Genet. 32:314-331 (1980). The restriction fragment lengthpolymorphism may create or delete a restriction site, thus changing thelength of the restriction fragment. RFLPs have been widely used in humanand animal genetic analyses (see WO 90/13668; WO90/11369; Donis-Keller,Cell 51:319-337 (1987); Lander et al., Genetics 121:85-99 (1989)). Whena heritable trait can be linked to a particular RFLP, the presence ofthe RFLP in an individual can be used to predict the likelihood that theanimal will also exhibit the trait.

Polymorphisms also exist as “short tandem repeats” (STRs) that includetandem di-, tri- and tetra-nucleotide repeated motifs. These tandemrepeats are also referred to as variable number tandem repeat (VNTR)polymorphisms. VNTRs have been used in identity and paternity analysis(U.S. Pat. No. 5,075,217; Armour et al., FEBS Lett. 307:113-115 (1992);Horn et al., WO 91/14003; Jeffreys, EP 370,719), and in a large numberof genetic mapping studies.

The term “allele(s)’, as used herein, indicate mutually exclusive forms(sequences) of a single polymorphic site or of a combination ofpolymorphic sites.

The term “single nucleotide polymorphism” (SNP), as used herein, is usedto indicate a polymorphism or genetic marker that involves a singlenucleotide. Typically, SNPs are bi-allelic polymorphisms/markers.

The term “indel”, as used herein, indicates an insertion/deletionpolymorphism that involves two or more nucleotides.

The term “major allele”, as used herein, refers to the most frequent oftwo or more alleles at a polymorphic locus.

The term “minor allele(s)”, as used herein, refers to the less frequentallele(s) found at a polymorphic locus.

The term “diploid”, as used herein, refers to the state of having eachchromosome in two copies per nucleus or cell.

The term “haplotype”, as used herein, denotes the combination of allelesfound at multiple contiguous polymorphic loci (e.g. SNPs) on the samecopy of a chromosome or haploid DNA molecule.

The term “genotype”, as used herein, indicates the allele or pair ofalleles present at one or more polymorphic loci. For diploid organisms,two haplotypes make up a genotype. For diploid inbred (plant or animal)species, which are principally homozygous, the genotype corresponds tothe haplotype.

The term “metatype”, as used herein, refers to an artificial haplotype.Metatypes originate from the replacement of the heterozygous calls in agenotype by either the minor or the major allele observed at theapplicable positions.

The term “sequence polymorphism cluster (SPC)”, as used herein, refersto a set of tightly linked (coinciding, co-occurring; co-segregating)sequence polymorphisms. More specifically, the term SPC indicates theset of coinciding minor alleles.

The term “cluster tag SNP(s)” (ctSNP), as used herein, refers to one ormore SNPs that best represent the sequence polymorphism cluster to whichthe SNP(s) belong and that are preferred as markers for the detection ofthat sequence polymorphism cluster.

The term “cluster tag polymorphism(s),” as used herein, refers to one ormore polymorphisms that best represent the sequence polymorphism clusterto which the polymorphisms belong and that can serve as markers for thedetection of that sequence polymorphism cluster. “Cluster tag SNP(s)”(ctSNP) are preferred cluster tag polymorphisms.

The term “SPC-haplotype”, as used herein, refers to the haplotype formedby those polymorphisms that belong to one or more SPCs.

The term “singleton”, as used herein, means an instance of a categorythat has only one element or occurs only once; the context makes clearwhat is meant. A singleton SNP or SPC occurs only once in the sampleunder investigation.

The term “clade”, as used herein, denotes a group of sequences orhaplotypes that are related in that these haplotypes have one or moreSPCs in common while also differing from one another in at least oneSPC.

SPC-algorithm

In the present invention a novel computational approach has beendeveloped for the identification of organizational features in sequencepolymorphisms. The present approach is different from the conventionalapproach for identifying haplotype blocks in that it does not look forblocks of contiguous polymorphisms that are in linkage disequilibrium,but rather determines the presence of clusters of sequence polymorphismsthat exhibit significant clustering statistics are searched. As such,clusters of the present invention can but need not be of contiguoussequences along a gene. The structures revealed by the method of thepresent invention are referred to as sequence polymorphism clusters(SPCs). These are groups of coinciding markers, i.e. sets of markersthat are co-inherited or that co-segregate (the latter term being morecommon in the agricultural sector). The alleles at such marker siteshave not been separated by recombination, gene conversion or recurrentmutation and have identical frequencies (a condition that can bedescribed as perfect or absolute LD). In this case, only two out of thefour possible two-site haplotypes are observed in the sample, i.e.observations at one marker provide complete information about the othermarker. In essence, SPCs are identified by first quantifying thepercentage coincidence between pairs of (bi-allelic) sites followed bythe stepwise assembly of marker alleles that exhibit coincidence above agradually less stringent threshold.

Coincident marker alleles can be identified with the use of certainmeasures for assessing the strength of LD. Many different LD statisticshave been proposed [Lewontin R. C., Genetics 140: 377-388, 1995; Devlin& Risch, Genomics 29: 311-322, 1995]. One frequently used LD measurethat is suitable with the present invention is r² (sometimes denotedΔ²). r² ranges from zero to one and represents the statisticalcorrelation between two sites; it takes the value of 1 if only two outof the four possible two-site haplotypes are observed in the sample. Thepopular |D′| statistic and similar measures [e.g. Q; see Devlin & Risch,Genomics 29: 311-322, 1995] are not appropriate for the presentalgorithm as these measures return the maximum value irrespective ofwhether there are two or three haplotypes formed by the pair of markers.

Adopting the standard notation for two loci—with a major (A,B) and aminor (a,b) allele at each site—r² is determined by dividing the squareof Lewontin's D value [Lewontin R. C., Genetics 49: 49-67, 1964] by theproduct of all four allele frequencies:r ²=(P _(ab) P _(AB) −P _(aB) P _(Ab))² /P _(a) P _(b) P _(A) P _(B)The notation for observed haplotype and marker allele frequencies isgiven in the 2×2 association Table 1. It should be kept in mind that theP-values are only sample estimates of some underlying unknownparameters. By the convention of naming alleles: P_(A)≧P_(a)≧P_(b).

TABLE 1 Notation for observed haplotype and marker allele frequenciesSite 1 Marker major allele A minor allele a Site 2 major allele B P_(AB)P_(aB) P_(B) minor allele b P_(Ab) P_(ab) P_(b) P_(A) P_(a) 1

The identification of clusters of coinciding markers can also beperformed with the use of other LD-measures [refer to Devlin & Risch,Genomics 29: 311-322, 1995], including Δ (the square root of Δ²), δ, andthe difference in proportions d:d=P _(ab) /P _(a) −P _(Ab) /P _(A)Yet another expression that was found useful is:C*=P _(ab) −P _(a) P _(b) /P _(a) −P _(a) P _(b)Similar to many other LD measures, the numerator of the above equationequals to Lewontin's D value [Lewontin R. C., Genetics 49: 49-67, 1964].The denominator, which serves to standardize D is however such that, incontrast to the more commonly used |D′| measure, C*=1, if, and only if,two out of the four possible two-locus haplotypes are observed in thesample. Note that the value of C* can be positive (coupling) or negative(repulsion) and that in this case absolute values are taken intoconsideration. The formula consistently used herein simply measures theproportion (%) of the haplotype consisting of the minor alleles a and b(P_(ab)), relative to the frequency of the most common minor allele(i.e. P_(a)):C=P _(ab) /P _(a)This formula has obvious shortcomings as a measure for LD mainly becausethe observed haplotype frequency P_(ab) is not offset against theexpected frequency such as in C*. For instance, C=0 whenever P_(ab)=0, asituation which does not necessarily imply there is linkage equilibrium.Conversely, C can be greater than 0 in case there is completeequilibrium, e.g. when all four haplotypes are equally frequent.Nevertheless, the formula is practical because of its transparency (i.e.the direct relation to the % coincidence) and is adequate when used incombination with appropriate threshold values.

The use of alternative formulas can yield different estimates of thestrength of association. Moreover, it is important to realize that atypical genetic variation data set contains a significant number ofmissing allele calls and that, consequently, haplotype and marker allelefrequencies may also be calculated in different ways which on itself mayalready have a marked effect on the returned value. In most cases thefrequency was estimated by simply dividing the observed number of aparticular allele or two-site haplotype by the total number of samples,thereby neglecting missing data. An alternative calculation consists ofthe ratio of the observed number of alleles/haplotypes over the totalnumber of unambiguous calls. According to a third method, the missingdata points were treated in a statistical way and were taken as both theminor and major allele in proportion to the observed allele ratio atthat polymorphic position. Similarly, the two-site haplotypes may alsooccur as fractions. In such a case, the number of alleles or haplotypeswas divided by the total number of samples. In yet another method onlythose samples that have an allele call at both polymorphic positions areconsidered to calculate the haplotype as well as the allele frequency.Note that, in this case, the allele frequencies at one particularpolymorphic site are not fixed but depend on the site with whichassociation is being calculated. The latter approach tends tooverestimate the strength of association and may be utilized for thedetection of SPCs in data sets with numerous missing allele calls. Itwill be understood that the different approaches are identical when thesample genotypes are devoid of missing data.

The following section provides a description of the elements of the SPCalgorithm/program. The input consists of a genetic variation tablecontaining the alleles present at a given number of polymorphic sites(columns) for a plurality of subjects (rows), i.e. basically a set ofhaplotypes (although it is shown herein that diploid genotype data mayalso be processed). The program can derive this table from a ‘multiplesequence alignment file’. The first step in the algorithm consists ofthe generation of a matrix with all pairwise calculations of thestrength of coincidence (e.g. values of C as defined above).Subsequently, a clustering operation is performed whereby one or moresequence polymorphism clusters (SPC) are formed and an SPC map isassembled. An SPC assembles sequence polymorphisms that coincide witheach other to an extent that exceeds an empirically defined thresholdlevel. The minimum number of polymorphisms that an SPC has toincorporate as well as its occurrence frequency in the sample in orderfor that SPC to be statistically meaningful varies from one data set tothe other.

The clustering operation is an iterative process. First, sequencepolymorphisms are grouped that exhibit absolute linkage, i.e. C=1 forall pairwise measurements. The clusters that are formed are allowed toexpand and new clusters are to emerge by gradually decreasing (e.g.using steps of 0.1, 0.05 or 0.025) the threshold value down to a bottomvalue. SPCs can be defined at any threshold value, including 1, ≧0.95,≧0.90, ≧0.85, ≧0.80, ≧0.75, ≧0.70, ≧0.65, ≧0.60, ≧0.55, and ≧0.50. Thoseof ordinary skill in the art will recognize that the adequacy of thethreshold settings depends, among other things, on the measure that isused to calculate the strength of association of the marker alleles.When using the measure C=P_(ab)/P_(a), the SPC maps are typicallygenerated at multiple threshold values between C=1 and C≧0.75. Theclustering operation may be performed according to several differentcriteria. In one approach, all pairwise coincidence values of thecluster polymorphisms must exceed the chosen threshold level.Alternatively, individual polymorphisms or entire clusters are mergedwhen the average association value exceeds a certain practical thresholdlevel. Yet another option requires that at least one polymorphism is inassociation with all other polymorphisms of the cluster above thethreshold value. As used herein, a cluster may assemble not only thegroup of primary polymorphisms whose pairwise association surpasses thethreshold but also secondary polymorphisms that are in association abovethe threshold with one of the primary polymorphisms.

It is important to realize that the C-measure only considers thehaplotype consisting of the minor alleles a and b (P_(ab)). This rendersthe formula less suited in cases where the allele frequencies are closeto 0.5. Also, mis-assignation of the minor allele can happen especiallyin small data sets, more specifically at polymorphic sites where theobserved frequency of the two alleles is exactly 0.5 or when as a resultof missing genotype data the apparent major allele is observed in lessthan half of the samples. In such cases both alleles need to be testedfor coincidence with other marker alleles. The SPCs that the program hasidentified can be visualized in a number of different ways including acolor-coded version of the above-mentioned matrix with coincidencevalues (C-values) and a color-coded version of the original inputgenetic variation table (sorted such that the individuals that share thesame SPCs are grouped). Several examples of the output, adapted forreadability in black/white illustration, are shown herein.

The SPC-program incorporates a module for the selection of cluster tagpolymorphisms. This selection is based on the identification of the oneor more polymorphisms that best represent the SPC they belong to.Typically, SNPs are chosen as cluster tag polymorphisms; cluster tagSNPs are herein also named ctSNPs. According to a preferred method, theaverage strength of association (herein also referred to as AverageLinkage Value or ALV) of each polymorphism with all other polymorphismsof the cluster is calculated and used as the decisive criterion: the oneor more polymorphisms/SNPs that exhibit the highest ALV are retained asmarkers for subsequent genotyping experiments.

In addition to most common bi-allelic SNPs, indels as well asmulti-allelic polymorphisms were sometimes included in the analyses.While multi-allelism is a rather rare event in humans it was encounteredoccasionally in the data sets that derive from highly polymorphicorganisms such as maize. When more than one minor allele was observed atan SNP site, the input genetic variation table containing the allelecalls (genotypes) at all the polymorphic sites for each individual wasadapted: the site was duplicated and modified so that each entry liststhe major allele in combination with one of the minor alleles while allother allele calls were replaced by blanks. The procedure ensures thatat each position in the table only two variants are observed. Unlessotherwise specified, indels were identified by two dots at,respectively, the start and the end position of the deletion. In betweenthese dots blank spaces may be present whenever polymorphic sites occurat intervening positions in the other samples. Blank spaces in thegenetic variation table are ignored and frequencies are calculated bysimply dividing the observed number of a particular allele or two-sitehaplotype by the total number of samples.

As disclosed herein, the algorithm can not only be applied to a data setof genetic variants from a particular locus but also, in a genericsense, to experimental data that capture all or part of that geneticvariation. The genetic variation table can also consist of diploidgenotype data. To process such a data set, the input table is adapted tocontain each individual twice; all heterozygous scores are then replacedby the minor allele in one entry and by the major allele in the secondentry. The resultant artificial haplotypes are herein named metatypesand the adapted genetic variation table is called a metatype table.

The present clustering method may presumably also be performed with theuse of other measures for the strength of association between markeralleles than those mentioned herein. These measures can either be knownor newly conceived. For instance, a statistic that measures the strengthof association between multi-allelic rather than bi-allelic loci couldbe utilized [e.g. refer to Hedrick P. W., Genetics 117: 331-341, 1987for a multi-allelic version of D′]. In general, the use of alternativemeasures in combination with appropriate threshold levels will expose aset of SPCs. This, and other variations in the algorithm may be readilyadapted by those skilled in the art. These variations may to a certainextent affect the output of the program (as is often the case withiterative clustering procedures) but are equally useful in exposing thefundamental SPC structure of genetic variation data—these variations aretherefore also within the scope of the present invention.

The algorithms of the invention also may be described according to FIGS.21-29. FIG. 21 is a schematic diagram of one possible embodiment of acomputer (i.e., machine) 30. The computer 30 may be used to accumulate,analyze, and download data relating to defining the subset of variationsthat are most suited as genetic markers to search for correlations withcertain phenotypic traits. The computer 30 may have a controller 100that is operatively connected to a database 102 via a link 106. Itshould be noted that, while not shown, additional databases may belinked to the controller 100 in a known manner.

The controller 100 may include a program memory 120, a microcontrolleror a microprocessor (MP) 122, a random-access memory (RAM) 124, and aninput/output (I/O) circuit 126, all of which may be interconnected viaan address/data bus 130. It should be appreciated that although only onemicroprocessor 122 is shown, the controller 100 may include multiplemicroprocessors 122. Similarly, the memory of the controller 100 mayinclude multiple RAMs 124 and multiple program memories 120. Althoughthe I/O circuit 126 is shown as a single block, it should be appreciatedthat the I/O circuit 126 may include a number of different types of I/Ocircuits. The RAM(s) 124 and programs memories 120 may be implemented assemiconductor memories, magnetically readable memories, and/or opticallyreadable memories, for example. All of these memories or datarepositories may be referred to as machine-accessible mediums. Thecontroller 100 may also be operatively connected to a network 32 via alink 132.

For the purpose of this description and as briefly discussed above, amachine-accessible medium includes any mechanism that provides (i.e.,stores and/or transmits) information in a form accessible by a machine(e.g., a computer, network device, personal digital assistant,manufacturing tool, any device with a set of one or more processors).For example, a machine-accessible medium includesrecordable/non-recordable media (e.g., read only memory (ROM); randomaccess memory (RAM); magnetic disk storage media; optical storage media;flash memory devices), as well as electrical, optical, acoustical orother form of propagated signals (e.g., carrier waves, infrared signals,digital signals); etc.

One manner in which an exemplary system may operate is described belowin connection with a number of flow charts which represent a number ofportions or routines of one or more computer programs. As those ofordinary skill in the art will appreciate, the majority of the softwareutilized to implement the routines is stored in one or more of thememories in the controller 100, and may be written at any high levellanguage such as C, C++, or the like, or any low-level assembly ormachine language. By storing the computer program portions therein,various portions of the memories are physically and/or structurallyconfigured in accordance with the computer program instructions. Partsof the software, however, may be stored and run on one or more separatecomputers that are operatively coupled to the computer 30 via a network.As the precise location where the steps are executed can be variedwithout departing from the scope of the invention, the following figuresdo not address which machine is performing which functions.

FIG. 22 is a flow chart 150 describing some of the steps used tofacilitate the production of a sequence polymorphism cluster (SPC) mapof a genomic region of interest. The flowchart 150 begins with the stepof obtaining the nucleic acid sequence of a genomic region of interestfrom a plurality of subjects (block 152). After obtaining the nucleicacid sequence, the flow chart 150 proceeds to identifying a plurality ofpolymorphisms in the nucleic acid sequences (block 154) and then toidentifying one or more SPCS, wherein each SPC comprises a subset ofpolymorphisms from the nucleic acid sequence wherein the polymorphismsof the subset coincide with each other polymorphism of the subset (block156). It should be noted that the identification of the one or more SPCsmay include identifying each polymorphism of the subset that coincideswith each other polymorphism of the subset according to a percentagecoincidence of the minor alleles of the polymorphisms of between 75% and100%. The identification of the one or more SPCs also may includemultiple rounds of coincidence analysis, wherein each successive roundof coincidence analysis is performed at a decreasing percentagecoincidence from 100% coincidence to 75% coincidence. Alternatively, thecoincidence of each of the polymorphism of the subset with each otherpolymorphism of the subset may be calculated according to a parameter,such as, for example, a pairwise C value, a r2 linkage disequilibriumvalue, and a d linkage disequilibrium value, wherein the pairwise Cvalue ranges from 0.75 to 1. It should also be noted that theidentification of a plurality of polymorphisms in the target nucleicacid sequences may be determined by an assay, such as, for example,direct sequence analysis, differential nucleic acid analysis, sequencebased genotyping DNA chip analysis, and PCR analysis.

FIG. 23 is a flow chart 160 describing some of the steps used tofacilitate the production of an SPC map of a genomic region of interestfrom unphased diploid genotypes. The flowchart 160 may begin with thestep of obtaining the unphased diploid genotypes of a genomic region ofinterest from a plurality of subjects (block 162). After obtaining theunphased diploid genotypes, the flow proceeds to determining the majorand minor metatypes found in the unphased diploid genotypes (block 164)and then to identifying one or more SPCs, wherein each SPC comprises asubset of polymorphisms from the metatypes wherein the polymorphisms ofthe subset coincide with each other polymorphism of the subset (block166). It should be noted that the step of identifying the one or moreSPCs may include identifying each polymorphism of the subset thatcoincides with each other polymorphism of the subset according to apercentage coincidence of the minor alleles of the polymorphisms ofbetween 85% and 100%.

As with the exemplary method of producing the SPC map described withreference to FIG. 22, the exemplary method disclosed in FIG. 23 mayinclude multiple rounds of coincidence analysis, wherein each successiveround of coincidence analysis is performed at a decreasing percentagecoincidence from 100% coincidence to 75% coincidence. Alternatively, thecoincidence of each of the polymorphism of the subset with each otherpolymorphism of the subset may be calculated according to a parameter,such as, for example, a pairwise C value, a r2 linkage disequilibriumvalue, and a d linkage disequilibrium value, wherein the pairwise Cvalue ranges from 0.75 to 1. It should also be noted that theidentification of a plurality of polymorphisms in the target nucleicacid sequences may be determined by an assay, such as, for example,direct sequence analysis, differential nucleic acid analysis, sequencebased genotyping DNA chip analysis, and PCR analysis.

FIG. 24 is an exemplary flow chart 170 describing some of the steps usedin a method of selecting one or more polymorphisms from a genomic regionof interest for use in genotyping. The flowchart 170 may begin with thestep of obtaining an SPC map of a genomic region of interest (block172). After obtaining the SPC map, the flow chart 170 may proceed toselecting at least one cluster tag polymorphism which identifies aunique SPC in the SPC map (block 174) and then to selecting a sufficientnumber of cluster tag polymorphisms for use in a genotyping study of thegenomic region of interest (block 176). It should be noted that thecluster tag polymorphism may be, for example, a single nucleotidepolymorphism (SNP), a deletion polymorphism, an insertion polymorphism;or a short tandem repeat polymorphism (STR). Also, the cluster tagpolymorphism may be a known SNP associated with a genetic trait.

FIG. 25 is a flow chart 180 describing some of the steps used tofacilitate the identification of a marker trait or phenotype. Theflowchart 180 may begin with the step of obtaining a sufficient numberof cluster tag polymorphisms from a genomic region of interest (block182). After obtaining the sufficient number of cluster tagpolymorphisms, the flow proceeds to assessing the cluster tagpolymorphisms to identify an association between a trait or phenotypeand at least one cluster tag polymorphism, wherein identification of theassociation identifies the cluster tag polymorphism as a marker for thetrait or phenotype (block 184). The cluster tag polymorphism may becorrelated with a variety of traits or phenotypes, such as, for example,a genetic disorder, a predisposition to a genetic disorder,susceptibility to a disease, an agronomic or livestock performancetrait, a product quality trait. Also, the marker may be a marker of agenetic disorder and the SPC map may be prepared according to the methoddescribed in FIG. 22, and the plurality of subjects each manifests thesame genetic disorder. It should also be noted that the identificationof the plurality of polymorphisms in the target nucleic acid sequencesmay be determined by a number of assays, including, for example, directsequence analysis, differential nucleic acid analysis, sequence basedgenotyping, DNA chip analysis and polymerase chain reaction analysis.

FIG. 26 is an exemplary flow chart 190 describing some of the steps usedto facilitate the identification of a location of a gene associated witha trait or phenotype. The flowchart 190 may begin with the step ofidentifying a plurality of SPCs identified in a given genomic regionassociated with the trait or phenotype, wherein each SPC comprises asubset of polymorphisms from the genomic region wherein thepolymorphisms of the subset are associated with each other polymorphismof the subset (block 192). After identifying the plurality of SPCs, theflow proceeds to identifying a set of cluster tag polymorphisms whereineach member of the set of cluster tag polymorphisms identifies a uniqueSPC in the plurality of SPCs (block 194). The flow may then continuewith assessing the set of cluster tag polymorphisms to identify anassociation between a trait or phenotype and at least one cluster tagpolymorphism, wherein identification of the association between thecluster tag polymorphism and the trait or phenotype is indicative of thelocation of the gene (block 196). It should be noted that the phenotypemay be, for example, a genetic disorder, a predisposition to a geneticdisorder, susceptibility to a disease, an agronomic or livestockperformance trait, or a product quality trait.

FIG. 27 is an exemplary flow chart 200 describing some of the steps usedin a method for in vitro diagnosis of a trait or phenotype. Theflowchart 200 may begin with the step of obtaining a marker for a traitor phenotype in a subject (block 202). After obtaining the marker, theflow proceeds to obtaining a target nucleic acid sample from the subject(block 204) and determining the presence of the marker for the trait ora phenotype in the target nucleic acid sample, wherein the presence ofthe marker in the target nucleic acid indicates that the subject has thetrait or the phenotype (block 206). The trait or phenotype may be, forexample, a genetic disorder, a predisposition to a genetic disorder,susceptibility to a disease, an agronomic or livestock performancetrait, or a product quality trait.

FIG. 28 is an exemplary flow chart 210 describing some of the steps usedin a method of determining the genetic identity of a subject. Theflowchart 210 may begin with the step of obtaining a reference SPC mapof one or more genomic regions from a plurality of subjects (block 212).After obtaining the reference SPC map, the flow proceeds to selecting asufficient number of cluster tag polymorphisms for the genomic regions(block 214) and obtaining a target nucleic acid of the genomic regionsfrom a subject to be identified (block 216). The flow may continue withdetermining the genotype of the cluster tag polymorphisms of the genomicregions of the subject to be identified (block 218) and comparing thegenotype of the cluster tag polymorphisms with the reference SPC map todetermine the genetic identity of the subject of interest (block 219).In some embodiments, the reference SPC map may be prepared according tothe methods described in connection with FIG. 22 or 23.

FIG. 29 is an exemplary flow chart 220 describing some of the steps usedin a method of determining the SPC-haplotypes from unphased diploidgenotype of a genomic region of interest. The flowchart 220 begins withthe step of obtaining an SPC map of a genomic region of interest (block222). After obtaining the reference SPC map, the flow proceeds todetermining the SPC-haplotypes from the SPC map, wherein eachSPC-haplotype includes a subset of SPCs from a genomic region whereinthe SPCs of the subset coincide (block 224) and identifying theSPC-haplotype of a test subject by comparing the SPC of the subject withthe SPC-haplotypes determined from the SPC map (block 226).

Genetic Polymorphisms are Often Organized in a Hierarchical SPCStructure

Using the computational approach described above, certain organizationalfeatures in sequence polymorphisms can be identified. When studiesreporting a relatively high marker density over contiguous regions areexamined, it can be noted that, in many of these genomic regions, a goodnumber of the SNPs (as well as indels) present are organized into one ormore sequence polymorphism clusters (SPC), i.e. sets of polymorphismsthat are essentially in absolute linkage (i.e. pairwise C-value is 1 orclose to 1). Several analyses indicate that, in general, the variousSPCs can comprise between 60% and 95% of all the polymorphisms presentin the sample under study. The inventors have found this to be true inall species for which sufficient data on genetic variation areavailable, including human, maize, Arabidopsis, Drosophila, and yeast.Typically, the polymorphisms in an SPC are non-contiguous and thepolymorphisms that belong to different SPCs are intermingled. Thepresent finding is different from the haplotype block concept in whichareas of contiguous polymorphisms are identified that are essentiallydevoid of recombination (i.e. high values of Lewontin's D′ measure)and/or that display limited haplotype diversity [refer to Wall &Pritchard, Nature Rev. Genet. 4: 587-597, 2003 for various definitionsof haplotype blocks].

The structures revealed by the method of the present invention arereferred to as sequence polymorphism clusters (SPCs). The most importantrecurrent characteristics of these SPC structures are exemplified inFIGS. 1 to 3. These Figures are based on idealized imaginary geneticvariation data sets (containing the allele calls at all the polymorphicsites for a plurality of test subjects), which are devoid of confoundingdata. The SPC structures observed in publicly available authentic datasets, derived from various species, are discussed in the Examplesprovided below. FIGS. 1A and 2A typify frequently observed patterns ofSPCs; in practice, mostly combinations of these two patterns are found(FIG. 3A). Groups of interspersed polymorphisms exhibit strong linkage,e.g. the alleles at the polymorphic sites are essentially found in onlytwo combinations. Matrices with all pairwise C-values are shown in FIGS.1B and 2B.

In the matrix of FIG. 1B, all SNPs belonging to the same SPC havepairwise values of C=1, while all SNPs belonging to the different SPCshave pairwise values of C=0. The few positions where C>0 reflect thelimited association of SPC-4 with the non-clustering SNP at position 33.In FIG. 2B it can be seen that all SNPs belonging to the same SPC havepairwise values of C=1, while all SNPs belonging to the different SPCshave pairwise values of C<1. The SPCs differ in the occurrence frequencyof the minor alleles in the population as well as the number ofcomponent SNPs. A fraction of the polymorphisms present do not exhibitthe tendency to cluster. These non-clustering polymorphisms are mostlyfound in conjunction with only one type of SPC.

The SPCs display one of two different relationships. Some SPCs areunrelated/independent, i.e. the minor alleles occur on distincthaplotypes (FIG. 1A). Other SPCs are dependent and can be rankedaccording to their level of inclusiveness; the minor allele of adependent SPC occurs on a subset of the haplotypes on which the minoralleles of one or more higher-level SPCs are found (FIG. 2A). As a rule,an SPC is not found both in conjunction with (dependent relationship),as well as separate from another SPC (independent configuration). Inother words, the minor alleles of two SPCs are not both found ondistinctive haplotypes as well as jointly on a third haplotype. Theorderly SPC structure can be represented by means of a simple networkwherein each branch corresponds to the appearance/disappearance of oneparticular SPC (see FIGS. 1C, 2C and 3B). When ignoring thenon-clustering polymorphisms, the nodes of the network correspond to thevarious sequences/haplotypes, which may or may not be observed in theplurality of samples under study (see for example FIG. 3B).

Haplotypes and their closest relatives that differ only by the presenceof non-clustering polymorphisms are herein named after the SPCs theycontain (see FIGS. 1A and 2A), and are herein referred to asSPC-haplotypes. The network clarifies the relationship between SPCs onthe one hand and haplotypes on the other hand: the SPCs can be viewed asthe elements with which the various haplotypes are built. Certain SPCsare specific to one haplotype while others are common to severalhaplotypes, thus defining a clade of related haplotypes. The SPCorganization translates into one of two different hierarchical networkstructures. Unrelated SPCs branch off from a single central point (FIG.1C); i.e. all of the ‘subsequences’ differ by one SPC from an apparentsource sequence. In the case of dependent SPCs, certain sequences havemoved away two or more SPCs from the point of reference (FIG. 2C). TheSPC network establishes an apparent genealogical relationship betweenthe main sequences, i.e. the sequences devoid of the non-clusteringpolymorphisms. It should be realized that the network is unrooted (dueto the lack of an “outspecies” or sequence from an accepted commonancestor) and, consequently, that evolutionary relationships deducedfrom the network are ambiguous. In the network representations, shownherein, the branches do not reflect evolutionary distance or extent ofsequence divergence while the size of the nodes does not relate to theoccurrence frequency of the various sequences. Various alternativerepresentations, that include a variable amount of evolutionaryinformation, are known in the art, such as a dendrogram and a cladogram.Skilled persons will also recognize that the network structure dependson the (depth of) sampling as well as the population under study.

The method of the present invention is thus capable of revealingintrinsic structures of DNA sequence variation in any species. Thisstructure stands out against and can explain the often complex patternsof LD between adjacent markers and the overall lack of correlationbetween the level of LD and physical distance. It was surprisinglydiscovered with the use of the present novel computational approach thatthe sequence variations, in for example maize, that previously had beendescribed as displaying very little LD [Tenaillon et al., Proc. Natl.Acad. Sci. USA 98: 9161-9166, 2001; Remington et al., Proc. Natl. Acad.Sci. USA 98: 11479-11484, 2001; Gaut & Long, The Plant Cell 15:1502-1505, 2003], are highly structured and that SPCs extend overgreater distances.

The haplotype notion and the more recently developed haplotype blockconcept [Daly et al., patent application US 2003/0170665 A1] representpractical approaches to capture most of the common genetic variationwith a small number of SNPs. However, until now, the essentially modularstructure of haplotypes and the genealogical record it provides has notbeen recognized. As set forth hereinafter, the knowledge of theunderlying SPC organization in a genomic region allows for the logicaland most powerful design and interpretation of genetic analyses.

Construction of an SPC-map

The method of the present invention is directed to an SPC map of agenomic region of interest or an entire genome and to methods ofconstructing such an SPC map. An SPC map can be used to select anoptimal set of markers, all or part of which can be assayed insubsequent genotyping studies, i.e. to establish an association betweena genotype and a phenotype/trait or for in vitro diagnostic purposes.The SPC map can also reveal the full breadth of genetic diversity in aspecies as well as its close relatives, such as certain economicallyimportant crops and livestock, and thereby provide opportunities formarker-assisted (inter)breeding. The SPC map can be constructed withgenetic variation data derived from any population sample. It isimportant however to realize that the SPC map depends to some extent onthe population under study as well as the depth of investigation (i.e.the size of the sample) and that the map should be used accordingly. Forexample, it will be clear that especially in a clinical diagnosticcontext, the value of certain assays is directly correlated with thevalidity and comprehensiveness of the SPC map on which the assays arebased and that, therefore, the map has to be built starting from arepresentative and sufficiently large sample of the population.

The construction of an SPC map comprises determining the pattern of SPCsacross the genomic region of interest, their relationship as well astheir boundaries. The pattern of SPCs is preferably analyzed at avariety of threshold levels rather than one single predeterminedstringency. SPCs can be defined at any threshold value, including 1,≧0.95, ≧0.90, ≧0.85, ≧0.80, ≧0.75, ≧0.70, ≧0.65, ≧0.60, ≧0.55, and≧0.50. Those of ordinary skill in the art will recognize that theadequacy of the threshold settings depends, among other things, on themeasure that is used to calculate the strength of association of themarker alleles. When measuring association as C=P_(ab)/P_(a), the SPCmaps are typically generated at multiple threshold values between C=1and C≧0.75.

In real life the identification of SPCs is confounded by the quality ofthe experimental data (missing and erroneous data) while, additionally,significant departures from the model SPC structure can occur as aresult of certain genomic processes (including recombination, geneconversion, recurrent mutation and back-mutation). These aspects make itdifficult to construct the SPC structure of a region in its fullestextent at one given threshold. For instance, at C=1 not all SPCs may berevealed, at least not to their full extent. At lower threshold values,on the other hand, certain SPCs may be merged. This is the case withpairs of dependent SPCs that have only minor differences in occurrencefrequency. In some cases, SPCs were observed that coincide on all exceptone single sample sequence (this is exemplified by the SPCs 1 and 1.1 inFIG. 2A). Such SPCs rapidly unite into one single SPC when the thresholdC-value is set lower than 1. This is illustrated in FIG. 2D/E: theseparate SPCs 1 and 1.1 observed at C=1 in FIG. 2A become one at C≧0.90.Thus, it is only through the assessment at multiple threshold valuesthat the complete SPC map can be constructed. However in most preferredembodiments, the lower threshold is C=0.75.

The effects of experimental deficiencies and the genomic processes onthe SPC map at different threshold values are discussed in more detail.A primary factor that may confound the analysis is the quality of thegenetic variation data. With state of the art genotyping technologies,especially under high-throughput conditions, a realistic error rate ofabout 0.5% may be achieved while the dropout rate in single passexperiments may be as high as 5-10%. It will be clear that missing orerroneous data points at a SNP position may eliminate that SNP from thecluster at a threshold value for C of 1 because the association will nolonger be perfect. The method of the present invention foresees ingradually lowering the threshold level so as to fully expose the SPCsstarting from the SPC-nuclei already recognized at C=1 and to recovercertain polymorphisms that were excluded at C=1. This is illustrated inFIG. 4. The genetic variation data set used for this figure is the sameas that for FIG. 1 except that 5% of the allele calls, chosen at random,were replaced by missing data (4.5%; symbolized by “N”) or an incorrectresult (0.5%; the accurate allele was substituted for the oppositeallele observed at that position). The SPCs identified at C=1, C≧0.9 andC≧0.75 are shown in FIGS. 4A, 4B and 4C, respectively.

The matrix of pairwise C-values is shown in FIG. 4D. It can be seenthat, by lowering the stringency, the largest part of the SNPs that donot cluster at C=1 can be recuperated. At C≧0.75 all but one of the SNPsof the five different SPCs are clustered (compare FIG. 4C with FIG. 1A).It is also of note that two dependent SPCs form at C=1, namely SPC-1.1and SPC-2.1 (FIG. 4E). These clusters are also present at C≧0.9 butmerge with SPC-1 and SPC-2 respectively at the C≧0.75 threshold (FIG.4F). This observation substantiates the necessity to examine SPCs atmultiple threshold levels.

In the present example distinct clusters are observed at C=1 that infact belong to the same SPC which becomes apparent at lower thresholdlevels whereas in other cases, illustrated in FIG. 2, certain genuineSPCs detected at C=1 may be overlooked at too low a threshold level.Inspection of the genotype data as well as the clustering at variousstringencies will generally reveal the most adequate threshold level forthe data at hand. Finally, it is possible that with certain data sets nosingle threshold value captures all of the SPCs and that the SPC map hasto be compiled from the analyses at various threshold values. Theinconsistencies and imperfections of the SPC map of a region, such asshown in FIG. 4C, can in turn be used to identify in a genetic variationdata set the most critical missing results as well as possible erroneousdata points. Thus, the present invention also encompasses a method toemphasize those data points that need experimental determination orverification in a repeat analysis.

In addition to data quality, the analysis of the genetic variation mayalso be confused by various known genomic processes, includingrecombination, gene conversion, recurrent mutation and back-mutation. Itshould be noted that some of these events cannot be distinguished fromexperimental errors. For example, back-mutations or recurrent mutationsmay equally well be interpreted as errors. All of the processes have theeffect of lowering the extent of association between certain markeralleles and may be dealt with by a careful analysis of the SPCstructures that are generated at a gradually decreasing stringency asdescribed above.

SPCs are primarily ended by recombination events. This is illustrated inFIG. 5 and FIG. 6. FIG. 5A/B exemplifies the effect of a few historicalrecombination events on the SPC structure. As a result of therecombination events, one particular SPC, namely SPC-1, is broken up inthree different SPCs at a threshold value of C=1. The recombinationevents are recognized by the simple fact that the SNPs of the new SPCs(e.g. SPC-1x and SPC-1y) do not intermingle with those of SPC1, as istypically be the case for SPCs in non-recombinant regions, and insteadproduce adjacent SPCs. Also, more often than not, a recombination eventresults in a violation of the prevailing principle in an SPC structure,namely that an SPC pair is not found both in an independency as well asa dependency configuration. In the case shown in FIG. 5, therelationship between the two new SPCs and SPC-1 is one of apparentdependency (this is because SPC-1 recombined with SPC-0 which is devoidof SPCs) and an irregularity is only observed when considering therelation between SPC-1x and SPC-1y. This conflict in the relationship isindicated by the dashed lines in the network structure of FIG. 5D. AnSPC map of the region at the C=1 threshold is shown in FIG. 5C. WhileSPC-1 is interrupted on both sides, the other SPCs are continuous andthe strength of association of sites that are not implicated in therecombination is unaffected. The significance of recombination in aparticular region—reflected either by the number of distinctiverecombination events and/or by the frequency in the population—can againbe assessed by examination of the clustering at lower threshold level.FIG. 5E/F and FIG. 5G/H show the identified SPCs and correspondingnetwork at C≧0.9 and C≧0.8, respectively. It can be seen that SPC-1x andSPC-1y unite one at the time with SPC-1 at stepwise decreasedstringencies. The merger of SPCs at lower threshold levels and,consequently, the reduction of the number of SPCs is valuable in that itreduces the number of genetic markers that are eventually needed tocapture the genetic diversity. This is especially important in thecontext of an association study because it allows the application ofthese markers in large cohorts at an affordable cost. The reduction inthe variation that is examined must however be balanced against thepotential loss in efficiency of the association study.

In contrast to the case of a small number of recombination events, FIG.6A/B shows that the association is low for all polymorphic site pairsthat are spanning a hotspot of recombination. It can be seen in thematrix of FIG. 6B that these pairwise C-values are all <0.5 indicatingthat there is no clustering between the SNPs on both sides of therecombination hotspot. Recurrent recombination clearly demarcates theend of an LD-region. FIG. 6C shows an SPC map of the locus of interest.The SPCs found in the two distinct regions are shown separately toreflect the fact that they can occur in various combinations.Additionally, SPCs that belong to neighboring regions do not obey thehierarchical principle that is observed within non-recombinant regions,namely that the minor alleles of two SPCs cannot both be found onseparate and the same haplotypes. In accordance with this, the SPCrelationship can only be shown for each region separately (FIG. 6D).

An SPC map differs significantly from the haplotype map described byDaly and coworkers for the human genome [Daly et al., patent applicationUS 2003/0170665 A1]. The haplotype map represents a ‘block-like’partitioning of the human genome. The discrete haplotype blocks aresegments of various sizes over which limited recombination is observedand which are bounded by sites of recombination. There is evidence tosuggest that within each such haplotype block the genetic diversity isextremely limited, with an average of three to six common haplotypesthat together comprise, on average, 90% of all chromosomes in thepopulation sample.

In an SPC map, in contrast to the haplotype map of Daly, the mapelements or SPCs in a region do not necessarily have the sameboundaries. In many instances, one or more SPCs extend across theendpoints of other SPCs (even so when that endpoint is observed at ahigh frequency in the population) or encompass multiple other SPCs. Themap elements are also defined differently: whereas haplotype blocksessentially correspond to non-recombinant regions, SPCs require the morestrict condition of co-occurrence of the marker alleles (absolute LD).Additionally, non-clustering polymorphic sites were initially regardedas poor markers in the SPC concept whereas, in the haplotype blockmodel, they were thought to be useful for inclusion in the panel of tagSNPs since they do contribute to haplotype diversity.

The inventors found regions where no SPC structure as described hereinis present in the genetic variation data or where the SPC structureexhibits flagrant departures from an orderly network hierarchy. Suchaberrations do not invalidate the present discovery and itsapplicability/utility. It should be noted that a data set might fail toreveal the intrinsic structure of the region under study when, forexample, the SNP data are insufficiently dense and/or contain too manyexperimental errors. Additionally, persons skilled in the art willappreciate that the failure to identify an inherent (coherent) structuremay not be readily explainable and may merely reflect the complexhistory of a locus. It will also be recognized that the number ofpolymorphisms that an SPC has to incorporate in order for it to beconsidered a genuine SPC very much depends on the data set at hand, moreparticularly on factors such as the SNP density, the number of samplesin which the SPC is observed, the organism under study, and the dataquality (see below).

To assess the statistical significance of SPCs detected at a giventhreshold, simulations can be run on a surrogate genetic variation tablewherein the allele calls at the various polymorphic sites are randomized(without affecting the allele frequencies). In particular data sets eventhe smallest clusters, consisting of only two polymorphisms, are to betaken into consideration. A related issue is the relevance of SPCs thatare observed only once in the sample under study. Indeed, sequencevariations that are unique for one individual will, by definition,display clustering. The observation may, however, be reliable especiallywhen (i) numerous polymorphisms are involved, and/or (ii) the event canbe rationalized. For example, singleton SPCs were encountered morefrequently in African individuals than in European samples which is inaccordance with the notion that Africans carry a wider variety ofhaplotypes than Europeans [Gabriel et al., Science 296: 2225-2229,2002].

The Rooting of SPC Networks

The SPC networks showing the hierarchical relationships between the SPCsrepresent unrooted phylogenetic trees. As a general rule, it is assumedin the representation of the SPC networks that the haplotype comprisingthe major allele at each SNP position corresponds to the root sequence.To obtain a bona fide phylogenetic tree, a comparison must be made withan outgroup species (i.e., a species that is closely related, and in thesame phylogenetic lineage as the species being examined but is not thesame as that species). For example, in the case of human, the mostobvious outgroup species comparison is with the chimpanzee sequence.Although the present version of the chimpanzee genome sequence stillcomprises a number of gaps, it is possible to align some selected humanregions (that display a clear SPC network) with the chimpanzee genomeand to score the chimpanzee alleles at the majority (˜95%) of the SNPpositions. From these analyses it is shown that most of the majoralleles of the SNPs in humans were identical to that of the chimpanzee.Additionally, in most cases where a different allele was found in thechimpanzee, that allele corresponded to the minor SNP allele and,importantly, essentially all these SNPs belonged to only one singleindependent SPC that derives from the SPC-0 sequence.

The comparison with the chimpanzee sequence is illustrated in FIG. 30for one particular human genomic region. This ˜112 kb region correspondsto part of the ENCODE block ENm014 and comprises 237 SNPs betweenpositions 126,499,999 and 126,612,618 of chromosome 7. The 237 SNPs weregenotyped in 30 trios, i.e. mother, father, and child. The SPC structurein this region is detailed in FIG. 30. In total 207 of the 237 SNPs wereclustered into 14 SPCs, which define 12 different SPC-haplotypes.Deconvolution of the 90 diplotypes revealed that 89 of these couldunambiguously be deconvoluted into the 12 SPC-haplotypes, and that 1 wasa recombinant haplotype. The 119 SPC-haplotypes computed from the 30trios are shown in FIG. 30. It can be seen that these 119 SPC-haplotypescan actually be grouped into 5 primary haplotypes, some of whichdiverged further into sub-haplotypes. Comparison with the chimpanzeesequence showed that the minor allele of 46 SNPs was actually ancestraland that, interestingly, 44 of these SNPs belonged to one single SPC(e.g. SPC-1; see FIG. 30). Note also that for 12 out of the 237 SNPpositions it was not possible to identify the matching base in thechimpanzee sequence—at these positions, it was assumed the chimpanzeesequence to correspond to the human major allele.

The finding that (part of the minor alleles of) one SPC is ancestral hasonly minor implications in that the bona fide phylogenetic tree is verysimilar to the SPC network (refer to FIG. 30). The SPC that contains twotypes of SNPs, depending on whether their major or minor allele isancestral, splits into two SPCs; these SPCs are denoted with the suffixM (major allele is ancestral) and m (minor allele is ancestral) in FIG.30. SPC-1, which comprises 75 SNPs, can thus be split into SPC-1m (44SNPs) and SPC-1M (31 SNPs). Note also that the two sets of SNPs,belonging to SPC-1M and SPC-1m, are clearly interlaced. In contrast tothe unrooted network where all SPCs denote groupings of minor alleles,the rooted tree contains the ancestral SNP alleles (alleles sharedbetween human and chimpanzee) at the root and incorporates an extra SPCthat is formed by the major alleles of the SNPs whose minor allele arefound in the ancestral sequence. In conclusion, the comparison with thechimpanzee sequence demonstrates that the SPC networks provide a goodapproximation of the true phylogeny, i.e. the relationships between theSPCs are only slightly affected by the rooting. More importantly, therooted and unrooted trees exhibit the same overall topology and validatethe notion that SPCs are to be viewed as ‘evolutionary units’. It wouldindeed appear that the present day haplotypes can be explained as havingevolved from the ancestral sequence in a punctuated mode, where eachevolutionary step is defined by a specific group or cluster of mutations(e.g. an SPC). In principle, any SPC (or part of the SNPs of that SPC)in the unrooted network can be ancestral without violating thephylogenetic relationship between SPCs on condition that the SPCs thatare higher up in the hierarchy are also ancestral.

The Selection of ctSNPs—Methodical Genetic Characterization of a Locus

The SPC map provides a rational and superior basis for the selection ofinformative SNPs that are of value in the discovery of associations withcertain phenotypes. First, it represents a coherent method to reduce thenumber of variants that need to be assayed without the loss ofinformation. Given the extent of linkage between the polymorphisms of anSPC, a single representative SNP, referred to as a ctSNP, can be chosento test for association while all other polymorphisms of the SPC can beconsidered redundant. In addition to this basic notion, it isanticipated that the difference between the polymorphisms that docluster and those that do not, will be highly relevant. The inventorsidentified cases where SPCs are shared between related species and,therefore, predate the speciation event (refer to Example 4). Thisobservation substantiates the idea that the SPCs are ‘very old’ andindicates that these structures represent ancestral groupings ofvariations that have been subjected to extensive natural selection andhave been retained throughout history because they effect or are linkedto a particular phenotype. Thus, SPCs may be viewed as most significantto test as units for association to phenotype. In contrast, thepolymorphisms that fail to cluster, even at relatively low stringency,are in all likelihood more recent mutations, in case they are found inconjunction with only one SPC, and may represent recurrent mutations incase the polymorphisms are in partial association with more than oneSPC. Whatever the molecular origin of these non-clusteringpolymorphisms, it was initially thought that the non-clusteringpolymorphisms had little or no value, but it has been determined hereinthat even the non-clustering polymorphisms are useful in the methodsdiscussed herein. It is therefore contemplated that the presentclustering approach represents a novel diagnostic method for the geneticdiagnosis of biologically (medically or agriculturally) relevant geneticvariation. More specifically, it is projected that the method of thepresent invention will be very useful for selecting DNA markers thathave superior diagnostic value.

Although an SPC may contain polymorphisms other than SNPs (see Example1), the polymorphism that is specified as a tag for the cluster willpreferably be an SNP. This type of marker is readily assayed using oneof several available procedures [Kwok P. Y., Annu. Rev. Genomics Hum.Genet. 2: 235-258, 2001; see also hereinafter]. The SNPs that belong toa particular SPC are not (all) equally useful as tag for that SPC. Thepossible concept that any one SNP that is in association with all otherpolymorphic sites of the SPC above a chosen threshold level qualifies asctSNP is to a large extent arbitrary. Instead, an objective ranking isproposed that reflects how well the various SNPs represent the SPC theybelong to. This can be achieved using one of several possiblecriteria—according to a preferred method the average strength ofassociation of each SNP with all other polymorphisms of the cluster isused as the decisive criterion. The strength of association was computedas C=P_(ab)/P_(a), where the allele and haplotype frequencies weredetermined following the most strict (i.e. statistical; refer to thesection ‘SPC-algorithm’) handling of missing data points. Thiscalculation method penalizes any missing data point as a deviation fromperfect linkage. The selection of ctSNPs according to this measure isillustrated for three different SPCs in FIG. 4G/H/I. The data set usedin FIG. 4 contains both missing as well as erroneous data points and theintended clusters can only for the largest part be exposed at the C≧0.75threshold (FIG. 4C). FIGS. 4G, 4H, and 4I show two tables for SPC-1,SPC-2 and SPC-4, respectively. The first summary table lists the allelecalls at each polymorphic site categorized in the respective SPCs. Thesecond table shows the matrix of pairwise C-values within each cluster.As indicated above, these values were calculated differently as comparedto those shown in FIG. 4D. The average C-value for each polymorphism isshown along the diagonal SNP as well as in the right margin. The mostpreferred ctSNP (or ctSNPs in case of an equal result) is that SNP withthe highest average strength of association with the other polymorphismsof the cluster. In general, several SNPs with only marginal differencesin the average strength of association with the other SPC polymorphismsmay be used interchangeably as ctSNP. This offers the opportunity toselect an SNP that is readily assayed on the platform of choice. Personsof ordinary skill in the art will appreciate that alternative ways canbe conceived to rank SNPs and to select tag SNPs that best represent acluster. It will also be understood that the validity of the choice ofctSNPs depends on the quality of the data. SNPs are justifiably rejectedas ctSNP when the relative weak association with the other polymorphismsis genuine, i.e. is attributable to biological phenomena such asrecurrent mutation or gene conversion. However, SNPs may also bedeclined inappropriately on the basis of poor assay results; it isobvious that the latter SNPs are in reality good candidate tag SNPswhich may be selected by using superior data obtained, for instance, bymeans of an alternative assay protocol/platform.

The SPC structure of a locus provides a logical framework that is of usein the design of experiments to genetically characterize that locus aswell as to rationalize the experimental results. Association between anSPC (or the ctSNP that represents the SPC) and a particular phenotypereveals itself by an increase in the frequency of the rare allele in apopulation that is characterized by the phenotype as compared to acontrol population. The relationships between SPCs also imply a certaincorrelation in the allele frequencies measured for the various SPCs. Forinstance, in the case of independent SPCs (FIG. 1A), an association ofthe phenotype with one specific SPC will be accompanied by a decrease inthe rare allele frequencies of (all) other SPCs. In contrast,associations with SPCs in a dependency relationship do coincide: acausal relation with one particular SPC necessarily implies linkage withthe lower-level dependent SPCs as well as linkage (albeit lesspronounced) with the SPCs that are higher up in the hierarchical tree. Aclade-specific SPC that is high up in hierarchy is shared by a number ofdifferent haplotypes and can, in principle, be used to reveal anassociation with any of these different haplotypes. This formalism—whichmay fail in case of synergy or antagonism between the alleles of thevarious SPCs—can help to assess the reliability of allele frequencymeasurements at a particular locus. In addition, the SPC network leadsto an insightful choice of ctSNPs in that it presents an objective wayto reduce the number of SNPs for use in genome wide association studieswith a minimum loss in information. First, SNPs can be chosen thatcorrespond to the primary level of divergence, e.g. SNPs that tag theSPCs labeled 1, 2, and 3 in FIG. 3B. A more thorough study would involvethe use of a larger number of SNPs, for example those that tag thesubsequent layer of dependent SPCs (e.g. SPCs 1.1, 1.2, 2.1, 2.2, 3.1and 3.2 in FIG. 3B). Such a more thorough study can be conducted eitherbecause the first search for association failed (the efficiency of anassociation study will indeed be related to the SPC level at which thestudy is performed) or to follow up on certain candidate SNPs that didshow linkage; in the latter case a certain part of the network isanalyzed in greater depth thereby exploring tag SNPs that correspond toall the subtle subdivisions in the structure. It is also important torealize that it is often not necessary to tag each individual SPC inorder to comprehensively characterize a locus. Indeed, certainclade-specific SPCs are redundant over the dependent SPCs in case theclade-specific SPC always co-occur with lower-level dependent SPCs. Inthis event, the clade-specific SPC corresponds to a node in the SPCnetwork that does not match with an actual sequences/haplotype in thesample under study. This is illustrated in FIG. 3B where the SPC-1 doesnot require tagging since it always coincides with either dependentSPC-1.1 or SPC-1.2 while, similarly, the detection of SPC-3.2.1 andSPC-3.2.2 render the identification of SPC-3.2 excessive.

A systematic genetic characterization is particularly useful for lociwith a complex SPC map. Analyses according to the methods of the presentinvention have revealed that certain loci are characterized by a highlybranched SPC structure with many levels of dependency (refer to FIGS. 3Aand 3B). This has, for example, been observed in the ‘SeattleSNPs’genetic variation data [UW-FHCRC Variation Discovery Resource;http://pga.gs.washington.edu/; see also Example 7]. It is to beanticipated that, in general, the recognition of such a highly divergentstructure will require a fairly exhaustive search for the geneticvariation by sequence determination of sizeable regions on a sufficientnumber of individuals, i.e. the variation data must be sufficientlydense and contain common as well as rare polymorphisms. Rare SPCs willonly progressively emerge as the population is being examined to agreater depth. For instance, while the data of the International HapMapProject, at the current level of SNP density [e.g. ˜274,500 SNPs as ofJan. 7, 2003; http://www.hapmap.org; Dennis C., Nature 425: 758-759(2003)], exhibit already some SPC structure, at least in the most SNPdense parts (refer to Example 9), it should not be expected to revealthis structure to its full depth.

The SPC structure and its translation into a methodical geneticcharacterization can be applied to genome wide scans and in addition, italso is applicable to other studies, such as in vitro diagnosis. One canenvisage that the stepwise genotyping may in certain cases beadvantageous in terms of cost. The diagnostically important human MHClocus constitutes but one possible example. Indeed, the followingExamples show an investigation of the MHC genotype data generated byJeffreys and coworkers [Jeffreys et al., Nature Genet. 29: 217-222(2001)] and show that at least certain regions are characterized by ahighly branched SPC network-(refer to Example 8).

SPCs Can be Identified on Diploid Genotype Data

In another embodiment, the method of the present invention is directedto the identification of SPCs and ctSNPs using diploid genotype data.Sequence polymorphism clusters may indeed be detected by applying thepresent algorithm directly to diploid genotypes in place of a haplotypedata set. This is less important for most economically important plantand animal species where essentially homozygous inbred lines are readilyavailable. However, the ability to use genotype rather than haplotypedata for the detection of SPCs represents an important advantage in thecase of humans. It avoids the need to determine the haplotypes, which ishard to accomplish experimentally and error prone when based oncomputational approaches alone.

The identification of SPCs on the basis of diploid genotype data isillustrated in FIGS. 7 and 8. The first example is based on essentiallythe same data set used in FIG. 1, i.e. a simple case of a number ofindependent SPCs. The second example relates to genotype data exhibitinga more complex SPC structure. To identify SPCs in diploid genotype data,the input genetic variation table (FIGS. 7A and 8A), which contains thegenotype calls at all the polymorphic sites for a multitude ofindividuals, is duplicated such that each sample is represented twice.This duplicate table is further modified in that all heterozygous scoresare replaced by the minor allele in one copy and by the major allele inthe second copy. The resultant artificial haplotypes are herein namedminor metatypes, in case the heterozygous calls are replaced by theminor allele, and major metatypes when the heterozygous calls in thediploid genotypes were substituted for the major allele. The duplicatedand reformatted genetic variation table is referred to as the metatypetable. It is noted that two essential features are perfectly retained inthe metatype format, namely the frequencies of the alleles and theirco-occurrence or linkage. Indeed, the ratios of the heterozygous andhomozygous alleles (i.e. 0.5:1) are correctly maintained by separatingdiploid genotypes in two metatypes. The linkages between theco-occurring sites are retained by the simultaneous replacement of allheterozygous genotypes on a single diploid genotype by either the minoralleles or the major alleles in respectively the minor and majormetatypes.

FIGS. 7B/C/D and 8B/C/D show the SPCs revealed by the analysis of thediploid genotypes. In both experiments, the diploid genotypes weregenerated by the random association of haplotypes with a known SPCstructure (FIGS. 7E and 8E). A comparison indicates that the SPCsidentified on the basis of diploid genotypes are identical to thosefound on the starting haplotypes. Thus, the analysis of the diploidgenotype data would ultimately lead to the selection of the same set ofctSNPs as an analysis of the elementary haplotypes. The illustrations ofFIGS. 7C/D and 8C/D however demonstrate one notable difference with bonafide haploid genotypes, namely that independent SPCs can coincide oncertain metatypes (compare FIG. 1A with FIG. 7C/D) and that consequentlythere is an apparent loss of the orderly structure. The skilled personwill realize that this is expected, given that diploid genotypes are thesum of two haplotypes and that the metatype table was generated by thearbitrary replacement of the heterozygous positions by either the minoror the major allele. The identification of SPCs starting from anauthentic human diploid genotype data set is demonstrated in theExamples section.

The methods of the present invention differ in several aspects from themethod developed by Carlson and coworkers to identify maximallyinformative tag SNPs [Carlson et al., Am. J. Hum. Genet. 74: 106-120,2004]. Initially, the present invention teaches a method to recognizesets of clustered polymorphisms in diploid genotype data. Thus, theselection of ctSNPs can be performed without the prior need to inferhaplotypes from these diploid genotype data (see Example 7). Incontrast, Carlson and coworkers base their calculation of the LD-measurer² on inferred haplotype frequencies. The experimental determination ofhaplotypes from unrelated diploid (human) individuals is very demandingwhile the computational probabilistic approaches have limitations inaccuracy. The present method avoids the possible errors in thecomputationally deduced haplotypes.

Secondly, the structure of genetic variation is, in the presentinvention, fully exposed on the basis of an examination of theassociation of marker alleles at different stringencies. In contrast,Carlson and coworkers consider bins of associated markers on the basisof a fixed statistic. It is amply demonstrated herein that any giventhreshold is data set dependent, and that association of markers at sucha threshold provide an incomplete and unrefined picture of the geneticvariation. This has practical consequences concerning the number, thecomprehensiveness, and the information content of the selected tag SNPs.For example, certain SNPs that do not exceed the chosen threshold ofassociation with any other SNP may unjustly be placed in singleton bins,which ultimately increase the number of tag SNPs that are required toprobe the genetic variation in a region.

Thirdly, Carlson and coworkers designate SNPs that are above thethreshold of association with all other SNPs of the bin as tag SNPs forthat bin; the tag SNPs are considered equivalent and anyone SNP can beselected for assay. A preferred method of the present invention entailsthe ranking of SNPs according to their suitability as tag SNPs (ctSNP)for the SPC.

Fourthly, in contrast with the one bin/one tagSNP concept of Carlson, itis amply demonstrated herein how the insight in the SPC structure, asrepresented by the network, allows the further reduction in the numberof tag SNPs with little or no loss in information. For example, thedetection of clusters that always co-occur with dependent SPCs areredundant over these dependent SPCs. Alternatively, an unrefinedanalysis may be performed by selecting tags for the clade-specific SPCsonly.

SPCs Can be Identified on the Basis of the Genotype of Sample Pools

In another embodiment, the method of the present invention is directedto the identification of SPCs and ctSNPs using genotype data obtained onpooled DNA samples. Similar to single samples, this genotyping of samplepools involves the simple scoring of the presence/absence of the allelicforms and does not require the quantification of the allele (frequency)in the pool. This application calls for a sensitive genotyping methodwhere allele frequencies of 10% (corresponding to a pool of five diploidindividuals), 5% (i.e. pool of ten diploid individuals) or even lowercan be detected. Several such methods are known in the art that permitthe unambiguous and reliable calling of an allele that is present as alesser species [Ross et al., BioTechniques 29: 620-629, 2000; Hoogendoomet al., Hum. Genet. 107: 488-493, 2000; Sasaki et al., Am. J. Hum.Genet. 68: 214-218, 2001; Curran et al., Mol. Biotechnol. 22: 253-262,2002; Blazej et al., Genome Res. 13: 287-93, 2003; Lavebratt et al., HumMutat. 23: 92-97, 2004]. The ability to compute SPCs and SPC maps fromgenotype data determined on sample pools represents a major advantage inthat it substantially reduces the cost of genotyping (e.g. by a factorof 5 to 10 or more). The SPC technology may therefore have a majorimpact on the mapping of genetic variation in human as well as otherspecies. A pooling strategy is not compatible with the aforementionedhaplotype block method, which relies on the genotyping of individualsfollowed by the deconvolution of the unphased diploid genotypes into thecomponent haplotypes.

The SNPs that are currently being mapped in the HapMap project representthe most common SNPs with high (>10%) population frequencies. In theHapMap project, the definition of haplotypes and haplotype blocks isbased on the genotype of individual DNA samples. However, for SNPs withlower population frequencies, e.g. in the 1% to 10% range, the number ofindividual samples that needs to be analyzed in order to observe theminor allele and to correctly infer the haplotype structure increasesconsiderably. This renders the inclusion of such low frequency SNPs inthe HapMap prohibitively expensive. As noted above, the unique featureof the SPC technology is that SPC maps can be deduced from the genotypeof pooled DNA samples. Depending on the allele frequencies, and the SNPgenotyping method used, it may be possible to analyze pools of 5, 10 ormore samples. In this way major cost savings can be achieved. This willbecome important when building the next generation human geneticvariation map, in which SNPs with lower population frequencies (1% to10%) will be mapped.

The identification of SPCs on the basis of the genotype of sample poolsis essentially identical to the methodology used for derivation of theSPCs from diploid genotype data. The input genetic variation tableconsists of the genotype calls (homozygosity for one of the alleles orheterozygosity) at all the polymorphic sites for a multitude of poolsinstead of a multitude of individuals. This input genetic variationtable is converted to a metatype table in the same way as is done fordiploid genotypes. A “metatype” is used to refer to a pseudo-haplotypederived from a diploid genotype. Briefly, the genetic variation table isduplicated such that the genotype of each sample-pool is representedtwice. The heterozygous calls are subsequently replaced by the minorallele in one copy and the major allele in the second copy. Theresultant artificial haplotypes are herein named minor metatypes, incase the heterozygous calls are replaced by the minor allele, and majormetatypes when the heterozygous calls were substituted for the majorallele. It is noted that the essential feature of allele co-occurrenceor linkage is perfectly retained in the metatype format.

Persons skilled in the art will readily realize that there is a relationbetween pool-size on the one hand and the frequency of the SPCs that canbe distinguished on the other hand. Indeed, in the case of large poolsand/or high-frequency SPCs, each individual pool will contain the minoralleles of all the frequent SPCs, which therefore can no longer bedifferentiated and will appear as one single SPC. The relation betweenpool-size and the ability to derive the correct SPC structure isillustrated in FIG. 31. For this in silico simulation study twoimaginary genetic variation tables consisting of 200 samples/haplotypeswere assembled. For the first table, the genotypes at the variouspolymorphic sites were chosen such that a total of nine independent SPCswith a frequency of 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20% and 25% arepresent. In the second table, the nine SPCs with the same frequenciesare in a dependency relationship. Starting from these reference datasets with known SPC structure, genetic variation tables were derivedthat list the genotypes of sample-pools. The pooling strategy consistedof the random combination of haplotypes as follows: 100 pools of 2haplotypes, 50 pools of 4 haplotypes, 20 pools of 10 haplotypes, or 10pools of 20 haplotypes. Each sampling was repeated 100 times. Finally,these genotype tables were converted to metatype tables and processedwith the SPC algorithm. FIG. 31 is a plot of the success rate (%; numberof times the SPC was detected in 100 simulation runs) with which thevarious SPCs are discerned given certain pool sizes. FIGS. 31A and 31Brefer to the independent and dependent SPCs respectively. Resultsessentially identical to those shown in FIG. 31 were obtained in anadditional series of simulation experiments where 100 diploid genotypeswere first generated through the random pairwise combination of the 200haplotypes and then assembled 50 pools of 2 diploid genotypes, 20 poolsof 5 diploid genotypes, or 10 pools of 10 diploid genotypes (data notshown). The results clearly demonstrate that it is possible tounambiguously identify the lower frequency SPCs on the basis of thegenotype of sample pools. A pooling strategy would thus ultimately leadto the selection of the same cluster tag polymorphisms for these SPCs asan analysis of the elementary haplotypes. The skilled personwill-realize that the analysis of sample pools—similar to the analysisof diploid genotypes—results in an apparent loss of the orderly SPCstructure in that independent SPCs can coincide on certain metatypes andthat the reconstruction of the SPC network becomes gradually moredifficult as the size of the pools increases.

FIGS. 31A and 31B demonstrate that the success rate of correct SPCidentification diminishes as the SPC frequency and/or pool sizeincrease. SPCs with a minor allele frequency of between 25 and 50% andpool sizes of greater than 20 were not included in the analysis; itseems clear however that in these cases the SPCs will be even moredifficult to discern. While it should be realized that the precisesuccess rate of SPC identification may depend on the context (i.e. whatother SPCs are present), it would appear from the above-discussedsimulation experiments that, in general, SPCs with a minor allelefrequency of between 1% and 10% can be identified with satisfactorysuccess using a pool-size of 10. Taken together, the results demonstratethat a practicable and cost-effective approach to construct an SPC mapwould consist of the genotyping of a collection of individual samples,permitting the identification of the most frequent SPCs, combined withthe analysis of a set of pools to allow the recognition of the lowerfrequency SPCs. The identification of SPCs using a pooling strategy onauthentic human diploid genotype data is demonstrated in the Examplessection.

The pooling strategy can be applied with genotyping methods thatcharacterize the sequence variations, but also it can be applied withexperimental approaches where the output reflects the genetic variationthat is present in the interrogated nucleic acid without actuallydetermining the full sequence or characterizing the variable positions.These approaches can be directed at either polymorphism discovery or thescoring of previously identified polymorphic sites. An example of suchan approach is the hybridization-based detection of polymorphismsdescribed hereinafter (refer to the section “SPC analysis on varioustypes of genetic variation data”). Experimental signals, rather than theexact underlying sequences, are equally well suited for theidentification of SPCs and ctSNPs using the SPC algorithm. Similar tothe case where the polymorphisms are identified, a distinction can bemade between relevant (i.e. clustering) and spurious (i.e.non-clustering) signals. An important advantage of these methods is thatdedicated assays for certain polymorphisms are not developed until aftertheir utility as SPC tags is demonstrated.

The identification of the SPCs in a genomic region suffices to proceedwith the selection of cluster tag polymorphisms as the most informativemarkers. While not imperative, it is in sometimes useful to ascertainthe relationship of the SPCs and to deduce the SPC network. Theestablishment of the SPC relation is less straightforward when based onthe unphased diploid genotype data (refer to the section ‘SPCs can beidentified on diploid genotype data’) and becomes even more complicatedwhen based on the genotype of sample pools. When SPCs are identified bymeans of a pooling strategy, their relationship can best be ascertainedby selecting one or more tag polymorphisms (ctSNPs) per SPC and typingthese tags in all the individual samples. The resultant genotypes can beused to establish whether the SPCs are in a dependent or an independentrelation according to the prevailing principle that independent SPCs arefound separately while a dependent SPC coincides with one or more otherSPCs. Again, this is less straightforward in case the individual samplesare of a diploid nature because then the genotypes are the sum of twohaplotypes which makes that independent SPCs can happen together (seealso ‘SPCs can be identified on diploid genotype data’). Nonetheless,when the data set consists of a sufficient number ofobservations/genotypes, it will, in general, be possible to decidewhether a tag always coincides with one or more other tags (i.e. the SPCis in a dependency relation) or is at least sometimes found on its own(independent relation).

Use of the SPC Structure to Infer Haplotypes

Also encompassed by the present invention is a method to unambiguouslyestablish the phase of the mutations starting from diploid genotype datawithout the need for supplementary experimental haplotype resolution.The in silico inference of haplotypes from diploid genotype data isillustrated by means of the aforementioned FIGS. 7 and 8. The exemplarygenotype data, assembled from known haplotypes, serve the purpose ofteaching the rationale used in the deconvolution of the genotypes. Asdiscussed above, the SPCs were already established directly from thegenotype data (see FIGS. 7C/D and 8C/D).

The example of FIG. 7 comprises a total of 8 haplotypes (FIG. 7E), 5 ofwhich correspond to independent SPCs 1 to 5, a sixth haplotype thatcontains no SPC (SPC-0 in FIG. 7E/F), and two additional ones, relatedto SPC-4 and SPC-0, that result from the presence of non-clusteringSNPs. As a consequence of the independence of the SPCs, i.e. theiroccurrence on separate haplotypes, it follows that the major metatypeswill contain not more than one type of SPC, whereas the minor metatypeswill comprise no SPC (in case of SPC-0 homozygosity), one SPC (in caseSPC-0 is one of the haplotypes) or two SPCs at most. This can be clearlyseen in FIG. 7C/D. The major metatypes contain the SPCs 1, 2, 4 and 5,and the minor metatypes exhibit various combinations of the differentSPCs (FIG. 7C/D). Note that the existence of SPC-3 can only be inferredfrom the minor metatypes. From these Figures it would—in the absence ofknowledge about the underlying haplotypes—be straightforward toascertain the independence of the SPCs and to deduce the SPC networkshown in FIG. 7F. That being established, the rules for thedeconvolution of the underlying haplotypes are simple. (1) If the minormetatypes contain only one SPC, then this genotype is deconvoluted intoone haplotype containing the SPC and one haplotype that contain no SPC(SPC-0). (2) If the minor metatypes contain two SPCs, then this genotypeis deconvoluted into one haplotype containing the first and a secondhaplotype containing the second SPC. SNPs that are not part of an SPCmay be phased as well. In the present example, this is the case for bothSNP-33 and SNP-38. The simplest interpretation, which can explain allgenotypes with the fewest haplotypes, is that SNP-33 is in partialassociation with SPC-4 only. Similarly, SNP-38 is associated with SPC-0since it found in minor metatypes containing either only SPC-0 or onesingle SPC. Alternative genotype data sets, assembled through randomcombination of the same haplotypes, did not always permit theunambiguous phasing of all non-clustering alleles. The skilled personwill realize that this limitation is inherent to the data at hand andnot a shortcoming of the deconvolution method per se.

The example of FIG. 8 aims to describe the deconvolution of more complexSPC structures, which are more likely to be encountered in practicalreality. The example comprises a total of 7 SPCs, of which 3 areunrelated/independent and 4 are dependent on them. These 7 SPCs occur on5 different haplotypes; an additional sixth haplotype contains no SPCs(FIG. 8E/F). In this case, contrary to the previous example, theresultant minor metatypes may comprise more than two SPCs, thusrequiring the prior establishment of the hierarchical relationshipsbetween the SPCs before the simple rules outlined above can be applied.By definition an SPC is dependent on another SPC if the SPC is alwaysco-occurring with that other SPC. Such co-occurrences can be deducedfrom inspection of both the major metatypes and the minor metatypes.While a co-occurrence in the major metatypes unambiguously establishesthat the SPCs are dependent, the dependency of an SPC may not beunequivocally ascertained on the basis of the minor metatypes because ofco-occurrence with multiple SPCs that are in an independent relation toone another. The likelihood to unambiguously determine the hierarchyincreases with the number of observations. For this reason, the SPCstructure is analyzed separately, first in the major and then in theminor metatypes.

Inspection of the SPCs observed in the major metatypes of FIG. 8C showsthat SPC 1.2 co-occurs with SPC-1 and that SPCs 2.1 and 2.2 co-occurcoincide with SPC-2, and thus unambiguously establishes thesedependencies. Inspection of the SPCs observed in the minor metatypes ofFIG. 8D shows that SPCs 1.1 and 1.2 always coincide with SPC-1 and thatSPCs 2.1 and 2.2 always coincide with SPC-2. The latter observationsconfirm the dependencies of SPCs 1.2, 2.1 and 2.2 deduced from the majormetatypes, and in addition establishes the dependency of SPC 1.1. Inthis case, the dependency of SPC 1.1 is unambiguous because the minormetatypes show all possible combinations of SPC 1.1 with the otherindependent SPCs 2 and 3. Inspection of the SPCs observed in FIG. 8C/Dshows yet another rule that is useful for interpreting and confirmingdependency relationships: when two SPCs that depend from the same SPCco-occur in minor metatypes, then the corresponding major metatypes willexhibit the SPC from which the two SPCs are dependent.

The above analysis demonstrates that even in the absence of knowledgeabout the underlying haplotypes, it is straightforward to establish therelationships between the SPCs and to deduce the SPC network shown inFIG. 8F from the data in FIG. 8C/D. Once the dependencies are resolved,the deconvolution can be performed by applying the rules outlined aboveon the independent SPCs (which in turn dictate the deconvolution of theappended dependent SPCs). As pointed out above, the number ofobservations at hand may in certain cases not suffice to unambiguouslydefine the SPC hierarchy. For example, in one particular replicatesimulation using another randomly generated genotype data set, SPC-1.1was always found together with both SPCs 1 and 2 making it impossible tounambiguously infer the dependency of SPC1.1. It will be realized thatthis is not a shortcoming of the present deconvolution method but rathera limitation that is inherent to the data. The skilled person will alsoappreciate that the present method can also be applied when theunderlying SPC structure is more complex than those shown in FIGS. 7Fand 8F and displays, for example, several more levels of dependency. Itshould be noted that the identification of SPCs starting from unphaseddiploid genotypes should not be performed at too low a stringency so asto prevent the coalescence of dependent SPCs, which would impair thecorrect deconvolution. Compared to other state-of-the-art computationalmethods for haplotype inference, the present method is accurate andscalable to large numbers of polymorphisms.

SPC Analysis on Various Types of Genetic Variation Data

The novel clustering approach of the present invention can be applied toany type of sequence or genetic variation data. In cases as documentedhere, it can be applied to sequence variations identified in DNAsequences of a specific locus derived from different individuals ofeither the same species or even different (related) species.Alternatively, the method can be applied to a set of closely linked SNPsscored in a number of individuals using state of the art genotypingmethods. In a generic sense the method can be used on any data set ofgenetic variants from a particular locus, like for instance onexperimentally observed variations that reflect but do not allowdefinition of the genetic differences in an interrogated target nucleicacid. Various experimental approaches are available for differentialnucleic acid analysis and to interrogate the sequence of a targetnucleic acid without actually determining the full sequence of thattarget or, in particular, the sequence at the variable positions. Forexample, hybridization of a test and a reference DNA sample to an arraycontaining thousands of unique oligonucleotides (termed features) mayreveal statistical differences in the hybridization intensity ofparticular features—such differential intensity signals need not beassigned to specific underlying sequence differences and can be used assuch with the method of the present invention. Similar to the case wherethe exact sequences at the polymorphic sites are known [supra], thepresent method allows discrimination between hybridization differencesthat are relevant—i.e. the clustered differences—and those that arespurious—i.e. the differences that do not cluster. The feasibility ofthe hybridization approach has been documented: Winzeler et al., Science281: 1194-1197, 1998; Winzeler et al., Genetics 163: 79-89, 2003;Borewitz et al., Genome Res. 13: 513-523, 2003. Arrays containing 25-meroligonucleotides that were primarily designed for expression analysishave been used to detect allelic variation (termed Single FeaturePolymorphism or SFP) via direct hybridization of total genomic DNA. SFPscould be discovered in yeast as well as in the more complex 120-MbArabidopsis genome. The main advantage of the method is that it uses farless features than the Variation Detection Arrays [VDAs; Halushka etal., Nat. Genet. 22: 239-247, 1999; Patil et al., Science 294:1719-1723, 2001]. VDAs tile every basepair along the chromosome andtherefore require a vast number of features (eight for each basepair),making the approach more expensive. Array hybridization is both apolymorphism discovery tool as well as a method for the routinegenotyping. There is no need to fully characterize the SFPs and toconvert them to dedicated assays using different array designs on thesame platform or using entirely different genotyping methodologies.

The preferred embodiment of DNA hybridization thus constitutes a novelmethod for genetic analysis in which the majority of the polymorphismsin a given DNA segment are recorded in a single assay, and aresubsequently analyzed using the present novel clustering approach so asto genetically diagnose the individual using the pattern of clusteredhybridization differences (refer to Example 11). In this respect, theDNA hybridization technology constitutes a genetic marker technologyhighly suited for determining the genetic state of a locus. Theadvantages of the above described hybridization approach for theidentification of the SPC structure in defined regions of a genome areas follows. First, the method does not require the systematic discoveryof the genetic variation that is present in a locus by full sequencedetermination using either conventional Sanger based methods or theabove-mentioned VDAs (‘sequence-by-hybridization). The hybridizationpatterns provide a sufficiently detailed record of the sequencevariation present and application of the present novel clusteringapproach will reveal a clustering in the hybridization signals similarto that observed when analyzing the sequence variations directly. Theskilled person will understand that the successful translation of thehybridization results to an SPC map requires that a sufficiently largenumber of features be used per locus. Secondly, the hybridizationreaction itself can be used for the routine determination of the allelicstate at various polymorphism clusters in a single assay, where theconventional approach would require the design and validation ofseparate assays for several ctSNPs per locus. The fact of being able torecord the greater part of sequence variations present offers a uniqueapproach for genotyping, which will in certain applications be of theuttermost importance.

Methods of Using SPC Maps

The methods of the present invention are particularly useful in twodistinct fields of application, namely for genetic analysis anddiagnosis in a wide range of areas from human genetics to markerassisted breeding in agriculture and livestock and for the geneticidentity determination of almost any type of organism.

The method of the present invention whereby the SPC structure of a locusis examined provides a logical framework for the design of superiorgenetic markers, ctSNPs. One important field of application of ctSNPswill be genome wide association studies in a variety of organisms. Inhuman for instance, the use of ctSNPs will be to identify geneticcomponents responsible for predispositions, health risk factors or drugresponse traits. In crop and live stock improvement the use of ctSNPswill be to identify genetic factors involved in quantitative traits thatdetermine agricultural performance such as yield and quality. It iscontemplated that ctSNPs may either lead to the identification of suchgenetic factors either indirectly through their linkage to the causativemutations in a nearby gene or directly through their association withcausative mutations that belong the same SPC. In this respect its isimportant to stress the major scientific finding that derives from theresults obtained with method of the present invention, namely that asubstantial fraction of the genetic variation found in nature isstructured in SPC modules that in certain cases comprise a large numberof different mutations. The mere existence of such SPC modules suggeststhat these have not arisen by chance alone, but rather representclusters of mutations that have been selected in the course of evolutionand hence represent allelic variants of genes that confer(ed) some kindof selective advantage to the species.

It is therefore contemplated that SPCs are likely modules of geneticvariation associated with traits, and complex traits in particular, andthis for the simple reason that these are determined not by singlemutations but rather by clusters of mutations. This is apparently thecase in one of the first quantitative traits recently characterized, theso called heterochronic mutations, namely mutations that affect thetiming of gene expression [Cong et al., Proc. Natl. Acad. Sci. USA 99:13606-13611, 2002].

The method of the present invention whereby the SPC structure of genomicregions is examined provides a logical framework for genetic identitydetermination. The SPC map of an individual will represent the ultimatedescription of the genetic identity of that individual, and this for anyorganism, from bacteria to humans. Consequently once the SPC map hasbeen determined for an organism, this logical framework allows thedesign of an exhaustive panel of ctSNPs that can be used to determine ordiagnose the genetic identity of individuals. While the utility of thisapplication in human in vitro diagnostics is particularly contemplated,numerous other applications of this technology also are envisioned. Forinstance, in the in vitro diagnosis of “identity preserved foods”,through the identification of the genetic material used in theproduction. Another application involves the identification of bacterialstrains, in particular pathogenic strains.

Simply by way of example, in human in vitro diagnostics, it iscontemplated that phenotypic traits which can be indicative of aparticular SPC include symptoms of, or susceptibility to, diseases ofwhich one or more components is or may be genetic, such as autoimmunediseases, inflammation, cancer, diseases of the nervous system, andinfection by pathogenic microorganisms. Some examples of autoimmunediseases include rheumatoid arthritis, multiple sclerosis, diabetes(insulin-dependent and non-dependent), systemic lupus erythematosus andGraves disease. Some examples of cancers include cancers of the bladder,brain, breast, colon, esophagus, kidney, leukemia, liver, lung, oralcavity, ovary, pancreas, prostate, skin, stomach and uterus. Phenotypictraits also include characteristics such as longevity, appearance (e.g.,baldness, color, obesity), strength, speed, endurance, fertility, andsusceptibility or receptivity to particular drugs or therapeutictreatments. Many human disease phenotypes can be simulated in animalmodels. Examples of such models include inflammation (see e.g., Ma,Circulation 88:649-658 (1993)); multiple sclerosis (Yednock et al.,Nature 356:63-66 (1992)); Alzheimer's disease (Games, Nature 373:523(1995); Hsiao et al., Science 250:1587-1590 (1990)); cancer (seeDonehower, Nature 356:215 (1992); Clark, Nature 359:328 (1992); Jacks,Nature 359:295 (1992); and Lee, Nature 359:288 (1992)); cystic fibrosis(Snouwaert, Science 257:1083 (1992)); Gaucher's Disease (Tybulewicz,Nature 357:407 (1992)); hypercholesterolemia (Piedrahita, PNAS 89:4471(1992)); neurofibromatosis (Brannan, Genes & Dev. 7:1019 (1994);Thalaemia & Shehee, PNAS 90:3177 (1993)); Wilm's Tumor (Kreidberg, Cell74:679 (1993)); DiGeorge's Syndrome. (Chisaka, Nature 350:473 (1994));infantile pyloric stenosis (Huang, Cell 75:1273 (1993)); inflammatorybowel disease (Mombaerts, Cell 75:275 (1993)).

Phenotypes and traits which can be indicative of a particular SPC alsoinclude agricultural and livestock performance traits, such as, amongothers, yield, product (e.g meat) quality, and stress tolerance

The present invention therefore defines a powerful framework for geneticstudies. Traditionally, association studies between a phenotype and agene have involved testing individual SNPs in and around one or morecandidate genes of interest. This approach is unsystematic and has noclear endpoint. More recently, a more comprehensive approach has beenpioneered which is based on the selection of a sufficiently dense subsetof SNPs that define the common allelic variation in so-called haplotypeblocks. The present invention reveals the more basic and fundamentalstructure in genetic variation. The SPC maps described herein canexplain the general observation that LD is extremely variable within andamong loci and populations and provide the basis for the most rationaland systematic genetic analysis of an entire genome, a sub-genomic locusor a gene. A subset of SNPs sufficient to uniquely distinguish each SPC(a ctSNP as described herein above) can then be selected andassociations with each SPC can be definitively determined by determiningthe presence of such a ctSNP. In this manner, the skilled artisan couldperform an exhaustive test of whether certain population variation in agene is associated with a particular trait, e.g., disease state.

Finally, the approach provides a precise framework for creating acomprehensive SPC map of any genome for any given population, human,animal or plant. By testing a sufficiently large collection of SNPs, itshould be possibly to define all of the underlying SPCs. Once these SPCsare identified, one or more unique SNPs associated with each SPC can beselected to provide an optimal reference set of SNPs for examination inany subsequent genotyping study. SPCs are therefore particularlyvaluable because they provide a simple method for selecting a subset ofSNPs capturing the full information required for population associationto find phenotype/trait-associated alleles, e.g., commondisease-susceptibility associated alleles. Once the SPC structure isdefined, it is sufficient to genotype a single ctSNP unique for a givenSPC to describe the entire SPC. Thus, SPCs across an entire genome orsub-genomic region can be exhaustively tested with a particular set ofctSNPs.

Particular methods of selecting, detecting, amplifying, genotyping anddata checking samples for use in the methods of the invention aredescribed in the Examples of this application. It should be recognized,however, that any suitable methods known to those of skill in the artcan be utilized. The following methods are further examples of methodsthat can be so utilized.

Non-clustering Polymorphisms

More often than not, a fraction of the polymorphisms present in agenomic region do not exhibit the tendency to cluster. As explainedhereinabove, this may to a certain extent be attributed to the qualityof the experimental data, more specifically missing or erroneousgenotypes, and to the choice of the threshold. It is thereforecontemplated in the present invention that the identification of SPCs ina data set involves the use of multiple threshold levels. However,detailed analyses of particular data sets show that some SNPs will notcluster at even the lowest threshold values and are truly standingapart.

While initially it was thought that non-clustering polymorphisms (seefor example discussion above) had little diagnostic value, surprisingly,it was found that in some cases (depending on for example the quality ofthe data set) the majority of the non-clustering polymorphisms can beunambiguously fitted into the SPC network constructed for the regionunder study. This implies that the non-clustering polymorphisms behaveas if they were ‘single-element-SPCs’. Similar to SPCs, a‘single-element-SPC’ is not found in conjunction with (dependentrelationship) as well as separated from another SPC (independentrelationship). The observation that many of the non-clusteringpolymorphisms conform to the network/phylogenetic tree was recurrentlymade in the case of human genomic regions that are essentially free ofrecombination events. This is exemplified in FIG. 32, which shows theSPC network of a particular region of the human genome, morespecifically the ˜44 kb segment of the ENCODE block ENm014 thatcomprises 94 SNPs and that runs from position 126,135,436 (rs#6950713)to 126,178,670 (Broad|BI192322) on chromosome 7. ENCODE regions arecharacterized by a high SNP density (e.g. about one SNP per 500nucleotides) and thus provide the best view on the ultimate structure ofgenetic variation in the human genome. In addition to a regular networkthat only includes the SPCs, FIG. 32 shows a second networkrepresentation that incorporates the non-clustering SNPs. Note also thatboth networks were rooted through comparison with the chimpanzeeoutspecies sequence (see hereinabove) and thus represent bona fidephylogenetic trees. It can be seen in FIG. 32 that 80 out of the 94 SNPswere clustered into 8 SPCs, representing 3 independent SPCs and 5dependent SPCs. These 8 SPCs define 6 different SPC-haplotypes. Of the14 SNPs that failed to cluster, 10 had an occurrence frequency of >1%.These 10 SNPs could be fitted unambiguously into the SPC network asshown in FIG. 32. In a similar vein the remaining non-clustering SNPscould also be fitted into the network but were omitted because of theirlow frequency (<1%).

One important aspect illustrated in FIG. 32 is that most of thenon-clustering SNPs (9 out of 10) define the exterior branches of thephylogenetic tree and occur at low frequency (a few % t), indicatingthat they represent recent mutations. The minor alleles of thesepolymorphisms are found in conjunction with only one type of SPC (but donot occur in all samples), and create minor variants/subdivisions of theevolved SPC-haplotypes. The finding that the non-clusteringpolymorphisms are mostly of recent origin corroborates the notion thatsuch markers are of inferior value (at least when searching forassociations with principal phenotypes or traits that were selected andmaintained throughout history).

Another important aspect illustrated in FIG. 32 is that a fraction ofthe non-clustering polymorphisms is higher up in the phylogenetic treeand appears to have arisen prior to the emergence of certain SPCs (1 outof the 10 non-clustering SNPs shown in FIG. 32). This category of‘single-element-SPCs’, in contrast to the recent/low frequencynon-clustering SNPs, may be included in the analysis of geneticassociation because these represent old genetic variants that have beenmaintained through balanced selection, and hence may be considered forselection as marker (outlined in the section “The selection ofctSNPs—Methodical genetic characterization of a locus”). Also, ingenomic regions that are essentially devoid of recombination, it isfrequently observed that SPCs and non-clustering polymorphisms that arehigher up in the phylogenetic tree appear to have undergonerecombination prior to the emergence of the dependent SPCs. Thisobservation is consistent with the proposed genealogy because oldermutations are more likely to have undergone recombination that morerecent mutations. The consequence of such ancient recombination eventsis that while the local networks around the ancient or ancestral SPCsand non-clustering polymorphisms are consistent, longer range networksmay exhibit more complex patterns of SPC dependencies, in which morerecently evolved SPCs simultaneously depend from more than one older SPCor non-clustering polymorphism. In certain cases, it appears that theemergence of the dependent SPCs correlates with one or more ancientrecombination events between the older SPCs or non-clusteringpolymorphisms. These observations lend further support to the notionthat the old SPCs or non-clustering polymorphisms may be functionallyimportant, and should be included in the analysis of geneticassociation.

In addition to the non-clustering polymorphisms that conform the orderlynetwork structure, part of the non-clustering polymorphisms (thepercentage is variable and depends on, for example, the genomic regionunder study) cannot be fitted unambiguously into the phylogenetic tree.In certain cases the underlying reasons are obvious. For instance, SNPslocated in regions where recurrent recombination is observed oftencannot be fitted into the networks on either side of the recombinationsite, and these obviously represent SNPs that whose linkage has beenscrambled by the recombination events. For some others it seems clearthat they may represent recurrent mutations. Examples of this type arethe single or multiple base deletions in homopolymer tracts, which areknown to be highly mutable (refer also to Example 1). In other cases,the observation may simply be caused by genotyping errors.

Additional instances where the majority of the non-clusteringpolymorphisms can be unambiguously fitted into an SPCnetwork/phylogenetic tree are given in Example 13.

In conclusion, it would appear that the SPC concept—which identifiesdiscrete sets of coinciding polymorphisms as evolutionary units—can beextended to include some or all of the non-clustering SNPs. Thiscomprehension has some important implications.

First, the non-clustering polymorphisms that comply with the networksystem can be included in the deconvolution of the unphased diploidgenotype data. As set forth hereinabove (see section “Use of the SPCstructure to infer haplotypes”), the SPC network structure represents atool to guide the deconvolution process. Inclusion of some or all of thenon-clustering polymorphisms will ultimately result in the derivation ofnot just the basic SPC-haplotypes but in a more refined andcomprehensive set of haplotypes that comprises both the olderpolymorphisms that are shared between the different SPC haplotypes aswell as some of the minor variants/subdivisions of the evolvedSPC-haplotypes.

Second, the extended network including some or all of the non-clusteringSNPs provides the ultimate description of the structure of thecomprehensive set of haplotypes found, and thus provides guidance forselecting a minimal set of tag SNPs for genetic association analysis. Asset forth hereinabove (see section “The selection of ctSNPs—Methodicalgenetic characterization of a locus”), the SPC map provides a rationalbasis for the selection of informative SNPs. One approach for selectinga minimal set of tag SNPs comprises selecting one tag SNP for each SPCor non-clustering polymorphism that is unique to each haplotype in thecomprehensive set. The information provided by the network specifiesprecisely which SPCs or non-clustering polymorphisms are unique to eachhaplotype, and which are shared between the different haplotypes. Thelatter information thus defines exactly which are the combinations oftag SNPs that represent these shared SPCs or non-clusteringpolymorphisms. As a consequence, this minimal set of tags will test thepossible association of a trait or phenotype with each and all SNPs thatare present in the set of haplotypes. Simply put, if an association isfound with only one of the tag SNPs, that result can be interpreted tomean that particular SPC or non-clustering polymorphism is associated,while a simultaneous association with a number of tag SNPs can beinterpreted to mean that the SPC or non-clustering polymorphism that isshared between the tagged haplotypes is associated. Persons skilled inthe art will realize that the ability to test the possible associationof a trait or phenotype with each and all SNPs present in the set ofhaplotypes is a unique and extremely valuable attribute of the method ofthe present invention, and that such is not provided for by thehaplotype block methods. Indeed, the haplotype block methods typicallygenerate simple listings of the different haplotypes found in aparticular region and select n-1 tag SNPs (where n-equals the number ofdifferent haplotypes) to differentiate the different haplotypes. Withoutthe knowledge of the underlying structure of these haplotypes obtainedusing the method of the present invention, it is impossible to interpretwhether simultaneous associations observed with two or more tag SNPs aremeaningful. If indeed older mutation(s) that are shared by differenthaplotypes are involved in a trait, such associations will not readilybe detected when using tag SNPs identified with the haplotype blockmethods.

Third, the identification of deviant or erroneous genotypes on the basisof inconsistencies in the SPC map of the region being considered can bealso be performed at non-clustering sites (as illustrated in Example13). As set forth hereinabove (see section “EXAMPLE 9 SPC map of HapMapSNPs of human chromosome 22”), the present invention also encompasses amethod to identify possible erroneous data points in a genetic variationdata set through the comparison of the actual genotypes of an individualsample with the network structure. Unexpected genotypes atnon-clustering sites are readily identified when the genotype at thosesites in one or more of the individual DNA samples prevents theunambiguous placement of the polymorphism in the network structure. Suchunexpected genotypes may be selected for experimental verification in arepeat analysis, and preferably the SNP should not be included in thecomputation of the haplotypes. A direct comparison of the haplotypescomputed with the method of the present invention and with the state ofthe art haplotype block methods (Haploview,http://www.broad.mit.edu/mpg/haploview/index.php) reveals that afraction of the haplotypes computed with the latter method are artifactsproduced by such erroneous genotypes. Persons skilled in the art willrealize that each genotyping error will result in an additionalhaplotype and that consequently data sets with very low error rates,such as the HapMap genotypes, will yield a sizable fraction of erroneoushaplotypes. Furthermore, since the haplotype block method selects onetag SNP for each haplotype, a fraction of the tag SNPs selected willcorrespond to SNPs that have yielded genotyping errors. With the methodof the present invention such genotyping errors are readily identified,and hence fewer and more accurate haplotypes are obtained whichconsequently yield fewer and more reliable tag SNPS.

Diagnosis of Non-clustering Disease Mutations

The present invention uncovers that SPCs represent discrete steps inevolution and are, for that reason, to be viewed as units that areuseful to test for association with particular phenotypes or traits. Itis however projected that certain causal mutations may not be part of anSPC, i.e. are non-clustering. This may for example be the case withso-called null-mutations and with the wide array of mutations in thegenes that were found to be associated with uncommon genetic disease(e.g. CFTR, BRCA, etc). In general, the rare mutations that underlie thehuman genetic disorders are relatively young [Rannala B. & BertorelleG., Human Mut. 18: 87-100, 2001]. It may be anticipated that many ofthese mutations will unambiguously fit into the SPC network of thedisease locus—as illustrated in the network representation shown in FIG.32, the mutations will be found in partial association with only one SPCand generate minor haplotype variants.

In the future, much effort will be directed towards the diagnosis ofthese disease-related genetic variations at the nucleotide level. Thediagnosis is however severely impeded by the growing number of suchdisease-related mutations. This necessitates the design and use of amultiplex assays series so as to reduce the effort and cost. The orderlySPC structure of the disease locus provides for an alternative strategyfor diagnosis. The approach would entail the exhaustive characterizationof the genetic variation followed by the construction of the SPCnetwork, which would reveal the genetic contexts in which the variousdisease mutations have arisen. While the details of the protocol woulddepend on the characteristics of the network structure at hand, one canenvisage that, in general, the diagnosis can be facilitated by firsttesting an appropriate set of SPCs and then to limit the subsequentexamination to that subset of disease mutations that is known to occurin combination with the SPCs that are actually present in the querysample. The number of SPCs that are selected for the initial testdepends on the network structure but should, as a rule, establishsufficient resolution so that the number of disease mutations that needsto be surveyed in (a) secondary assay(s) is considerably reduced andoutweighs the effort of the primary test.

Methods of Identifying SNPs

The present inventors have demonstrated the feasibility and desirabilityof building a map of a genome (region) in which the SPCs are defined.This SPC map contains sets of co-occurring alleles, e.g., cosegregatingpolymorphisms. Within an SPC map there may be one or more SPCs and eachSPC may be further identified by a polymorphism that is characteristicof that particular SPC. Using such SPC maps, sequence variation can becaptured by a relatively small number of SNPs. Of course, acomprehensive description of the SPC map in a human, animal or plantpopulation can require a high density of polymorphic markers. Across thegenome of the human as well as some other (model) species a rapidlygrowing number of polymorphisms is available and these data may be usedto produce the SPC maps described herein. However, in certaincircumstances, it may be desirable to identify new SNPs and/or togenotype previously known SNPs in additional samples of the same or adifferent population. This can be readily achieved using methods knownin the art.

A. Sample Population

Polymorphism information can be obtained from any sample population toproduce a map of the invention. “Information” as used herein inreference to sample populations is intended to encompass data regardingfrequency and location of polymorphisms and other data such asbackground and phenotypic (e.g. health) information useful in genotypestudies and the methods and maps of the invention described herein. Insome cases it can be desirable to utilize a diverse (multiethnic)population sample. Such a sample can include a total random sample inwhich no data regarding (ethnic) origin is known. Alternatively, such asample can include samples from two or more groups with differing(ethnic) origins. Such diverse (multiethnic) samples can also includesamples from three, four, five, six or more groups. In other cases itcan be desirable to utilize a homogeneous (monoethnic) sample in whichall members of the population have the same (ethnic) origin. Ethnicityrefers to the human case and can be, for example, European, Asian,African or any other ethnic classification or any subset or combinationthereof. In the case of plant or animal genetic studies, the populationscan consist of breeding germplasm, specific races, varieties, lines,accessions, landraces, introgression lines, wild species or any subsetor combination thereof. The population samples can be of any sizeincluding 5, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100, 125, 150 or moreindividuals.

Information for producing a map of the invention can also be obtainedfrom multiple sample populations. Such information can be usedconcurrently or sequentially. For example, studies can be performedusing homogeneous (monoethnic) population samples. The results of thesestudies can then be utilized with the results of a study on a diverse(multiethnic) sample. Alternatively, the results from the homogeneous(monoethnic) sample can be combined to form a diverse (multiethnic)study.

B. Sample Preparation

Polymorphisms can be detected from a target nucleic acid from anindividual being analyzed. For assay of human genomic DNA, virtually anybiological sample may be used. For example, convenient tissue samplesinclude whole blood, semen, saliva, tears, urine, fecal material, sweat,buccal, skin and hair have readily been used to assay for genomic DNA.In the case of plants, any part (e.g. leaves, roots, seedlings) can beused for genomic DNA preparation. For assay of cDNA or mRNA, the samplemust be obtained from an organ or tissue in which the target nucleicacid is expressed.

Many of the methods described below require amplification of DNA fromtarget samples. Amplification techniques are well described in theliterature. For example, PCR is a generally preferred method foramplifying a target nucleic acid, See generally PCR Technology:Principles and Applications for DNA Amplification (ed. H. A. Erlich,Freeman Press, NY, N.Y., 1992); PCR Protocols: A Guide to Methods andApplications (eds. Innis, et al., Academic Press, San Diego, Calif.,1990); Mattila et al., Nucleic Acids Res. 19:4967 (1991); Eckert et al.,PCR Methods and Applications 1, 17 (1991); PCR (eds. McPherson et al.,IRL Press, Oxford); and U.S. Pat. No. 4,683,202 (each of which isincorporated by reference for all purposes).

Other suitable amplification methods include the ligase chain reaction(LCR) (see Wu and Wallace, Genomics 4:560 (1989); Landegren et al.,Science 241:1077 (1988)), transcription amplification (Kwoh et al.,Proc. Natl. Acad. Sci. USA 86:1173 (1989)), and self-sustained sequencereplication (Guatelli et al., Proc. Nat. Acad. Sci. USA 87:1874 (1990))and nucleic acid based sequence amplification (NASBA). The latter twoamplification methods involve isothermal reactions based ontranscription, which produce both single stranded RNA (ssRNA) and doublestranded DNA (dsDNA) as the amplification products in a ratio of about30 or 100 to 1, respectively.

C. Detection of SNPs in Target DNA

There are two distinct types of analysis depending whether or not apolymorphism in question has already been characterized. The first typeof analysis is sometimes referred to as de novo characterization andmakes use of a differential nucleic acid analysis. This analysiscompares target sequences in different individuals to identify points ofvariation, i.e., polymorphic sites. By analyzing a group of individualsrepresenting the greatest variety characteristic patterns of alleles canbe identified, and the frequencies of such alleles in the populationdetermined. Additional allelic frequencies can be determined forsubpopulations characterized by criteria such as geography, race, orgender. The second type of analysis is determining which form(s) of acharacterized polymorphism are present in individuals under test. Thereare a variety of suitable procedures for sequence-based genotyping,which are discussed in turn.

Allele-Specific Probes and Primers. The design and use ofallele-specific probes for analyzing SNPs is described by e.g., Saiki etal., Nature 324:163-166 (1986); Dattagupta, EP 235,726, Saiki, WO89/11548. Allele-specific probes can be designed that hybridize to asegment of target DNA from one individual but do not hybridize to thecorresponding segment from another individual due to the presence ofdifferent polymorphic forms in the respective segments from the twoindividuals. Hybridization conditions should be sufficiently stringentthat there is a significant difference in hybridization intensitybetween alleles, and preferably be selected such that a hybridizingprobe hybridizes to only one of the alleles. Some probes are designed tohybridize to a segment of target DNA such that the polymorphic sitealigns with a central position (e.g., in a 15 mer at the 7 position; ina 16 mer, at either the 8 or 9 position) of the probe. This design ofprobe achieves good discrimination in hybridization between differentallelic forms.

Allele-specific probes are often used in pairs, one member of a pairshowing a perfect match to a reference form of a target sequence and theother member showing a perfect match to a variant form. Several pairs ofprobes can then be immobilized on the same support for simultaneousanalysis of multiple polymorphisms within the same target sequence.

In allele-specific polymerase chain reaction (PCR) analysis, theallele-specific primer hybridizes to a site on target DNA overlapping aSNP and only primes amplification of an allelic form to which the primerexhibits perfect complementarity. See Gibbs, Nucleic Acids Res. 17:2427-2448 (1989). This primer is used in conjunction with a secondprimer which hybridizes at a distal site. Amplification proceeds fromthe two primers leading to a detectable product signifying theparticular allelic form is present. A control is usually performed witha second pair of primers, one of which shows a single base mismatch atthe polymorphic site and the other of which exhibits perfectcomplementarily to a distal site. The single-base mismatch preventsamplification and no detectable product is formed. The method works bestwhen the mismatch is included in the 3′-most position of theoligonucleotide aligned with the polymorphism because this position ismost destabilizing to elongation from the primer.

Tiling Arrays. The SNPs can also be identified by hybridization tonucleic acid arrays (DNA chip analysis). Subarrays that are optimizedfor detection of variant forms of precharacterized polymorphisms canalso be utilized. Such a subarray contains probes designed to becomplementary to a second reference sequence, which is an allelicvariant of the first reference sequence. The inclusion of a second group(or further groups) can be particular useful for analyzing shortsubsequences of the primary reference sequence in which multiplemutations are expected to occur within a short distance commensuratewith the length of the probes (i.e., two or more mutations within 9 to21 bases). Methods and compositions for making such subarrays are wellknown to those of skill in the art, see e.g., U.S. Pat. No. 6,368,799,which describes methods of detecting gene polymorphisms and monitoringallelic expression employing a probe array.

Direct Sequencing. The direct analysis of a sequence of any samples foruse with the present invention can be accomplished using either thedideoxy-chain termination method or the Maxam-Gilbert method (seeSambrook et al., Molecular Cloning, A Laboratory Manual (2nd Ed., CSHP,New York 1989); Zyskind et al., Recombinant DNA Laboratory Manual,(Acad. Press, 1988)).

Sequencing by Hybridization. A well-recognized alternative to usingdirect-sequencing is the use of sequencing by hybridization (SBH), amethod by which the sequence of a target nucleic acid is reconstructedfrom a collection of probes to which the target nucleic acid sequencehybridizes. Methods and compositions for sequencing by hybridization aredescribed, e.g., in U.S. Pat. No. 6,689,563; U.S. Pat. No. 6,670,133;U.S. Pat. No. 6,451,996; U.S. Pat. No. 6,399,364; U.S. Pat. No.6,284,460, U.S. Pat. No. 6,007,987; U.S. Pat. No. 5,552,270. Each ofthese documents are incorporated herein by reference as providing ateach of the methods and compositions for making and using SBH chips forSBH analyses.

Denaturing Gradient Gel Electrophoresis. Amplification productsgenerated using the polymerase chain reaction can be analyzed by the useof denaturing gradient gel electrophoresis. Different alleles can beidentified based on the different sequence-dependent melting propertiesand electrophoretic migration. Erlich, ed., PCR Technology, Principlesand Applications for DNA Amplification, (W. H. Freeman and Co, New York,1992), Chapter 7.

Single-Strand Conformation Polymorphism Analysis. Alleles of targetsequences can be differentiated using single-strand conformationpolymorphism analysis, which identifies base differences by alterationin electrophoretic migration of single stranded PCR products, asdescribed in Orita et al., Proc. Natl. Acad. Sci. USA 86, 2766-2770(1989). Amplified PCR products can be generated as described above, andheated or otherwise denatured, to form single stranded amplificationproducts. Single-stranded nucleic acids may refold or form secondarystructures which are partially dependent on the base sequence. Thedifferent electrophoretic mobilities of single-stranded amplificationproducts can be related to base-sequence difference between alleles oftarget sequences.

Allele-specific Primer Extension—Minisequencing. A primer isspecifically annealed upstream of the SNP site of interest, which maythen be extended by the addition of an appropriate nucleotidetriphosphate mixture, before detection of the allele-specific extensionproducts on a suitable detection system. If dideoxynucleotidetriphosphates labelled with different dyes are used, single baseextension (SBE) products can be analyze by electrophoresis using afluorescent sequencer, either gel or capillary based. Conventionaldetection methods, such as an immunochemical assay, can also be used todetect the SBE products. Alternatively, Matrix-assisted laser desorptionionisation time-of-flight mass spectrometry (MALDI-TOF-MS) can be usedto separate the extension products as well as the primer to a highdegree of precision by their respective molecular masses without theneed for any labelled tags [Storm et al., Methods Mol. Biol. 212:241-262, 2003]. In pyrosequencing [Nyrén et al., Anal. Biochem. 208:171-175, 1993] complementary strand synthesis is performed in theabsence of dideoxynucleotides. Each dNTP substrate is added individuallyand incorporation is monitored by the release of pyrophosphate which isconverted to ATP fuelling a luciferase reaction. If the dNTP is notincorporated, it is degraded with no light emission. The sequence ofevents is followed and is specific to the sequence of the variant.

Allele-specific Oligonucleotide Ligation. For an oligonucleotideligation assay (OLA), two primers are designed that are directly next toeach other when hybridized to the complementary target DNA sequence inquestion. The two adjacent primers must be directly next to each otherwith no interval, or mismatch, for them to be covalently joined byligation. This discriminates whether there is an SNP present. There aremany different labelling and detection methods, including ELISA[Nickerson et al., Proc. Natl. Acad. Sci USA 87: 8923-8927, 1990], orelectrophoresis and detection on a fluorescence sequencer.

Allele-specific Cleavage of a Flap-Probe. This assay, called Invader,uses a structure-specific 5′ nuclease (or flap endonuclease) to cleavesequence-specific structures in each of two cascading reactions. Thecleavage structure forms when two synthetic oligonucleotide probeshybridise to the target. The cleaved probes then participate in a secondgeneric Invader reaction involving a dye-labelled fluorescence resonanceenergy transfer (FRET) probe. Cleavage of this FRET probe generates asignal, which can be readily analysed by fluorescence microtitre platereaders. The two cascading reactions amplify the signal significantlyand permit identification of single base changes directly from genomicDNA without prior target amplification [Fors et al. Pharmacogenomics 1:219-229, 2000].

Linkage Analyses

The genomic maps and the methods of the invention can be readily used inseveral ways. The mapping of discrete regions which contain sequencepolymorphisms permits, for example, the identification of phenotypesassociated with particular SPCs, the localization of the position of alocus associated with a particular phenotype (e.g. a disease) as well asthe development of in vitro diagnostic assays for (disease) phenotypes.

For example, linkage studies can be performed for particular SPCsbecause such SPCs contain particular linked combinations of alleles atparticular marker sites. A marker can be, for example, a RFLP, an STR, aVNTR or a single nucleotide as in the case of SNPs. The detection of aparticular marker will be indicative of a particular SPC. If, throughlinkage analysis, it is determined that a particular ctSNP is associatedwith, for example, a particular disease phenotype, then the detection ofthe ctSNP in a sample derived from a patient will be indicative of anincreased risk for the particular disease phenotype. Additionally, if aparticular phenotype is known to be associated with a particulardiscrete SPC, then the locus can be sequenced and scanned for codingregions that code for products that potentially lead to the diseasephenotype. In this manner, the position of a disease-susceptibilitylocus of a disease can be located.

Linkage analysis can be accomplished, for example, by taking samplesfrom individuals from a particular population and determining whichallelic variants the individuals have at the marker sites that tagdiscrete SPCs. Using algorithms known in the art, the occurrence of aparticular allele can be compared to, for example, a particularphenotype in the population. If, for example, it is found that a highproportion of the population that has a particular disease phenotypealso carries a particular allele at a particular polymorphic site—thenone can conclude that the particular allele is linked to the particularphenotype in that population. Linkage analyses and algorithms for suchanalyses are well known to those of skill in the art and exemplarymethods are described in greater detail in e.g., U.S. Pat. No. 6,479,238(see especially section IV therein). Additionally, since the markeralleles embody discrete SPCs, the phenotype is also determined to belinked to a discrete SPC. Thus, by using genetic markers, e.g., ctSNPs,that tag discrete SPCs, linkage analysis can be performed that allowsfor the conclusion that a particular phenotype is linked to a particularSPC.

The foregoing aspects of the invention are further described by theExamples hereinafter.

EXAMPLE 1 Intraspecies SPC Map of the sh2 Locus of Maize

The present example provides proof of concept that the methods of thepresent invention can be used to generate an SPC map of a complete genelocus that has been sequenced in a number of individuals of a particularspecies. Many studies on the genetic diversity of specific genes havebeen conducted in a broad range of plant and animal species, and thesesequences are publicly available from GenBank(http://www.ncbi.nlm.nih.gov). In most of these studies relatively shortgene segments, less than 1000 bp, have been sequenced and only in a fewstudies have complete genes been sequenced. From the available completeor near complete gene sequences available in GenBank, the shrunken2(sh2) locus from maize was chosen to exemplify the different aspects ofthe invention. The published shrunken2 locus sequences from 32 maizecultivars (Zea mays subsp. mays) comprise a region of 7050 bp containingthe promoter and the coding region of the sh2 gene [Whitt et al., Proc.Natl. Acad. Sci. USA 99: 12959-12962, 2002].

The sequences for this analysis were retrieved from GenBank(http://www.ncbi.nlm.nih.gov) accession numbers AF544132-AF544163. Thesequences were aligned using ClustalW [Thompson et al., Nucleic AcidsRes. 22: 4673-4680, 1994] and the alignments around the indels weremanually optimized. Using a perl script all the polymorphic sites in thealigned sequences were scored to generate a genetic variation table inwhich each column represents a polymorphic site and each row representsa sample. In the columns the corresponding alleles (bases) in eachsample are represented, except for indels that are represented by twodots at respectively the start and the end position of the deletion.When more than two (minor) alleles were found at a polymorphic site,this polymorphic site was duplicated such that each column containedonly one of the minor alleles, and replacing the other minor allele(s)by a blank. Note that the number of polymorphic sites in the geneticvariation table is larger than the number of variable positions in thesequence because of the indels and multi-allelic sites.

The genetic variation table of the sh2 gene comprises 212 polymorphicsites. To simplify the analysis and the representation of the results,the singletons, i.e. the polymorphic sites at which the minor alleleoccurs only once, three recombinant genotypes and the duplicate indelsites were excluded from the analysis. This reduced the number ofpolymorphic sites in the genetic variation table to 141. From thiscompacted genetic variation table the SPCs that comprise 3 or morepolymorphic sites were computed with the SPC algorithm using thefollowing thresholds: C=1, C≧0.90, C≧0.85, C≧0.80 and C≧0.75. At thethreshold of C≧0.80 (shown in FIG. 9A) the algorithm clustered a totalof 124 polymorphic sites (88%) of the sh2 locus into 9 different SPCs,most of which extended throughout the entire locus. The five largestSPCs comprise between 10 and 39 polymorphisms (note that not allpolymorphisms are displayed in FIG. 9A). The sh2 locus thus yields acontinuous SPC map, as is shown in FIG. 9A. The figure shows the SPCs in29 of the 32 non-recombinant individuals. The uninterrupted SPC map ofthe 7 kb sh2 locus indicates that the locus has experienced fewhistorical recombination events. This is further supported by theobservation that only 3 of the 32 samples sequenced appear recombinant.

Apart from the identification of the overall SPC structure of the sh2gene, the present example serves to illustrate a number of specificaspects of the present invention. First the example provides a clearillustration of the two types of relationships that can exist betweenSPCs, namely independence or dependence of the SPCs. It can be seen fromFIG. 9B that the sh2 locus comprises 5 primary independent SPCs, eachcomprising a large number of different polymorphisms (SPCs 1, 2, 3, 4,and 9). In addition, several layers of dependency can be observedinvolving SPCs 9, 5, 8, 6, and 7. When taking also the SPCs comprisingtwo polymorphisms and the SPCs comprising the singletons into account,several additional dependent SPCs are found (not shown). Consequently,the SPC-network of FIG. 9B is a simplified representation of the SPCstructure of the sh2 locus. Furthermore, it can be anticipated that theactual SPC structure of the sh2 locus of maize may be even more complex,because the number of individuals that has been sequenced is relativelysmall, and hence may represent only a fraction of the full geneticdiversity of the maize (Zea mays subsp. mays) germplasm.

A second important aspect concerns the mutations that do not cluster:only 17 of the 141 polymorphic sites could not be clustered at thethreshold of C≧0.80. A sample of non-clustering polymorphic sites isshown in the left part of FIG. 9A. Analysis of these polymorphic sitesrevealed that these comprise three types. First, some polymorphic sitesare associated with only one SPC but do not occur in all samples, andthus presumably represent more recent mutations. The second typecomprises polymorphic sites that are found associated with more than oneSPC. For some of these it seems clear that they represent recurrentmutations. Examples of this type are the single or multiple basedeletions in homopolymer tracts, which are known to be highly mutable.The third type comprises polymorphic sites that are associated with twoor three different SPCs. Some of these may represent ancestral mutationsthat are common to these SPCs. However, irrespective of the explanationfor the lack of clustering, the non-clustering polymorphisms wereinitially thought to represent a subset of the polymorphic sites with anerratic association of poor diagnostic value. However, as demonstratedherein the non-clustering polymorphisms also for a useful aspect of theSPC networks of the present invention. Consequently, this analysisdemonstrates that the methods of the present invention provide aselection of polymorphic sites exhibiting superior diagnostic value,thus providing proof of concept for one of the principal utilities ofthe method of the invention, namely the selection of genetic markers foranalyzing genetic traits.

A third aspect of the present example concerns the thresholds forcalculating the SPCs. As outlined above the SPC analysis was performedon a subset of samples comprising the 29 non-recombinant samples. At athreshold of C=1, 121 of the 141 polymorphic sites were clustered.Lowering the threshold to C≧0.80 added 3 additional polymorphic sites tothe SPCs. These were three SNP that had one aberrant data point. In thiscase the use of lower thresholds had marginal effects. The reasons forthis are several; For one, the sequences were obviously of high quality,and the frequency of erroneous allele calls was low. Second, byexcluding the recombinants prior to clustering, the analysis was biased.

A fourth aspect emerging from our analysis is that the SPCs of the sh2locus comprise both indels and SNPs, supporting that the method ofclustering captures all mutational events. In addition, analysis ofmulti-allelic polymorphic sites shows that some of these representindependent mutations of the same position that are linked to differentSPCs. The latter is illustrated by the polymorphism at position 5154 inFIG. 9A.

A fifth aspect concerns the design of cluster tag SNPs. Since most SPCsare defined by large numbers of markers that are in absolute linkage,the choice of tag SNPs in this case is straightforward. The only remarkis that one should avoid using any of the 3 markers that are not inperfect linkage. The SPC network shown in FIG. 9B has considerablepractical utility for the selection of genetic markers for geneticanalysis of the sh2 locus. While there is a total of 9 SPCs, it is clearthat a genotyping study can, depending on the desired level ofresolution, address a subset of these SPCs. For instance, a genotyping,could be limited to the ctSNPs that tag the 5 primary independent SPCs(i.e. SPCs 1, 2, 3, 4, and 9). Even for an exhaustive analysis of thelocus only a subset of the SPCs would have to be addressed, morespecifically SPCs 1, 2, 3, 4, 5, 6, and 7 because the clade-specificSPCs 8 and 9 are redundant over the dependent SPCs.

EXAMPLE 2 Intraspecies SPC Map of the sh1 Locus of Maize

The present example provides proof of concept that the methods of thepresent invention can be used to generate an SPC map of a complete genein which extensive recombination has occurred. This example presents ananalysis of the polymorphic sites in the shrunken1 (sh1) locus frommaize to exemplify further aspects of the invention. The publishedshrunken1 locus sequences from 32 maize cultivars (Zea mays subsp. mays)comprise a region of 6590 bp containing the promoter and the codingregion of the sh2 gene [Whitt et al., Proc. Natl. Acad. Sci. USA 99:12959-12962, 2002].

The sequences for this analysis were retrieved from GenBank(http://www.ncbi.nlm.nih.gov) accession numbers AF544100-AF544131. Thesequences were aligned to generate a genetic variation table asdescribed in detail in Example 1. The genetic variation table of the sh1gene comprises 418 polymorphic sites. Because of this very large numberof polymorphic sites, the singletons were excluded from the analysis.This reduced the number of polymorphic sites to 282. From this compactedgenetic variation table the SPCs that comprise 3 or more polymorphicsites were computed with the SPC algorithm using the followingthresholds: C=1, C≧0.90, C≧0.85, C≧0.80 and C≧0.60. At the threshold ofC≧0.80 (see FIG. 10) the algorithm clustered 145 polymorphic sites (51%)of the shl locus into 26 SPCs. This result is quite different from thatobtained with the sh2 locus in Example 1, and illustrates thatpolymorphisms in this locus can exhibit a strikingly differentstructure.

In contrast to the sh2 locus from Example 1, in which ˜90% of thepolymorphic sites were clustered, only ˜50% of the sh1 polymorphic sitescould be clustered. While the sh2 locus yielded a relatively smallnumber of SPCs comprising many polymorphic sites, the sh1 locus yieldeda much larger number of SPCs containing on average fewer polymorphicsites. Furthermore, as can be seen from FIG. 10, most of the SPCsidentified were located in two segments (positions 1186 to 3283 and 3559to 5243) comprising about half of the locus, and a third very short (120bp) highly polymorphic segment (positions 6315 to 6436; not shown). Thesh1 locus thus yields a discontinuous SPC structure, which isrepresented in FIG. 10. It is evident that the observed SPC structuremust be the result of recurrent recombination (or recombinationhotspots), in the regions between the segments exhibiting a clear SPCstructure. These recombination events not only generated the twodistinct segments but also scrambled the polymorphic sites within theintervening regions such that none of these polymorphisms cluster, andthis even at thresholds of C≧0.60. Finally it can be seen from FIG. 10that recombination has occurred within the two segments exhibiting aclear SPC structure. This is particularly evident in the right segmentwhere most SPCs are short

The two contrasting Examples 1 and 2 illustrate that the methods of thepresent invention can be used to generate informative SPC maps of geneloci, irrespective of the recombination history of the locus. Thestructure of the resulting SPC maps is determined primarily by therecombination frequency in the region of interest. Extensiverecombination within a locus will result in a fragmented SPC structurewith short range SPCs containing fewer polymorphic sites, while in theabsence historical recombination, the locus will yield a highlycontinuous SCP map with SPCs comprising large numbers of polymorphicsites and extending over longer distances. Irrespective of the SPCstructure of the locus, the methods of the present invention have clearpractical utility. In both cases the methods of the present inventionprovide a selection of polymorphic sites exhibiting superior diagnosticvalue, thus providing proof of concept for one of the principalutilities of the method of the invention, namely the selection ofgenetic markers for analyzing genetic traits. While in the sh2 case amere 7 ctSNPs will suffice to capture the majority of the geneticvariation within the locus without loss of information, the ctSNPsselected for genotyping the sh1 locus will cover only a fraction of thegenetic variation within the locus. Persons skilled in the art willunderstand that this is an intrinsic limitation and not one related tothe method of the present invention.

EXAMPLE 3 Intraspecies SPC Map of the Y1 Locus of Maize

The present example provides proof of concept that the method of thepresent invention can be used to generate an SPC map of a locus in whichseveral historical recombination events have occurred. This examplepresents an analysis of the polymorphisms in the Y1 phytoene synthaselocus of maize to exemplify further aspects of the invention. The Y1phytoene synthase gene, which is involved in endosperm color, wassequenced in 75 maize inbred lines [Palaisa et al., The Plant cell 15:1795-1806, 2003], comprising 41 orange/yellow endosperm lines and 32white endosperm lines.

The sequences for this analysis were retrieved from GenBank(http://www.ncbi.nlm.nih.gov) accession numbers AY296260-AY296483 andAY300233-AY300529. The sequences comprise 7 different segments from aregion of 6000 bp containing the promoter and the coding region of theY1 phytoene synthase gene. The individual sequences were aligned togenerate 7 genetic variation tables as described in detail in Example 1,which were subsequently combined into a single genetic variation table.The combined genetic variation table of the Y1 phytoene synthase genecomprises 191 polymorphic sites. The SPCs that comprise 3 or morepolymorphic sites were computed with the SPC algorithm using variousthresholds. The algorithm clustered 85, 95 and 113 polymorphisms at athreshold value of C=1, C≧0.95 and C≧0.80, respectively.

The Y1 SPC map presented in FIG. 11B shows the SPCs obtained at thethreshold value of C≧0.95, with in the upper half of the panel the whiteendosperm lines and in the lower half of the panel the orange/yellowendosperm lines. While the orange/yellow lines all share the samecontinuous SPC (SPC-1), the white lines exhibit a number of differentSPCs, exhibiting a discontinuous pattern of SPCs. This pattern isconsistent with a relatively small number of recombination events thatoccurred at the positions between the different SPCs, indicated by thearrows in FIG. 11B.The present example also illustrates one importantaspect of the present invention, namely that SPCs may be highlycorrelated with phenotypes. Indeed the finding that all orange/yellowendosperm lines share the same SPC indicates that the polymorphisms thatmake up that SPC are either tightly linked to or are responsible for theorange/yellow phenotype.

The present example also illustrates another important aspect of thepresent invention, namely the importance of using different thresholdsto identify SPCs. At the threshold of complete linkage, the SPCs includeonly those polymorphisms that are present in non-recombinantindividuals, since the polymorphisms that are affected by (rare)recombination events will not exhibit complete linkage. In the presentexample, the only mutations within the single SPC present in theorange/yellow lines that are perfectly correlated with the phenotype arethe polymorphisms at positions 3-701 and 3-755, which are the only onespresent in InbredLo32 (see FIG. 11B), which moreover is a complexrecombinant. This illustrates that while SPCs may be well correlatedwith phenotypes, not all polymorphisms in the SPC have necessarily thesame diagnostic value.

EXAMPLE 4 Interspecies SPC Map of the Globulin 1 Locus of Maize

The present example provides proof of concept that the methods of thepresent invention can be used to generate an interspecies SPC map of agene locus that has been sequenced in individuals from different closelyrelated species. This example presents an analysis of the polymorphicsites in the globulin 1 (glb1) locus of maize to exemplify furtheraspects of the invention. Evidence is presented that the SPCs detectedby the method of the present invention may have arisen before the splitof the related species and can therefore be considered ancient.

The globulin 1 gene sequences analyzed in the present example have beengenerated in phylogenetic studies on the origins of domesticated maize[Hilton and Gaut, Genetics 150: 863-872,1998; Tenaillon et al., Proc.Natl. Acad. Sci. USA 98: 9161-9166, 2001; Tiffin and Gaut, Genetics 158:401-412, 2001] and comprise a region of 1200 bp containing part of thecoding region of the glb1 gene from 70 different accessions of maizeinbred lines and landraces (Zea mays subsp. mays), the progenitor ofcultivated maize (teosinte or Zea mays ssp. parviglumis), and theclosely related species Zea perennis, Zea diploperennis and Zealuxurians.

The sequences for this analysis were retrieved from GenBank(http://www.ncbi.nlm.nih.gov) accession numbers AF064212-AF064235,AF377671-AF377694 and AF329790-AF329813. The sequences were aligned togenerate a genetic variation table as described in detail in Example 1.The genetic variation table of the glb1 gene comprises 317 polymorphicsites of which 66 were singletons. Because the primary interest of thisanalysis was to examine the polymorphic sites that were shared betweenthe samples, the singletons were excluded from the analysis. Theremaining 251 polymorphisms were clustered with the SPC algorithm usingthe following thresholds: C=1; C≧0.90, C≧0.85, C≧0.80 and C≧0.75.Inspection of the SPC map of the globulin 1 gene showed that in themajority of the samples the SPCs were uninterrupted throughout the gene.Analysis of the haplotypes revealed that 31 samples exhibited historicalrecombination and gene conversion events, and consequently these wereexcluded from the analysis. The clustering analysis was repeated on thesamples exhibiting continuous SPC structures using the same thresholds.At the lowest threshold of C≧0.75 a total of 99 polymorphisms wereclustered in a total of 14 SPCs with 3 or more polymorphisms percluster. Of these, 3 were rejected that could not be represented in thenetwork structure (see FIG. 12B). The SPC map of the globulin 1 gene,visually represented in FIG. 12A, shows that 5 primary SPCs can groupall 39 sequences: SPC-1 and SPC-5 comprise different Zea maysaccessions, SPC-2 comprises both Zea mays and Zea diploperennisaccessions, SPC-3 comprises the Zea luxurians accessions and SPC-4comprises the Zea perennis accessions, and can be further subdividedthrough the various dependent SPCs. Close inspection of FIGS. 12A and12B shows that the SPCs are in general, but not always, specific for thedifferent Zea species. In particular in the SPC-4 group two Zea maysaccessions (landraces CHH160 and GUA14, denoted by the red arrows inFIG. 12A) were found to exhibit identical SPC maps to the Zea perennisaccessions, respectively SPC-4.1 and SPC-4.2. 1. The fact that theshared SPCs comprise a large number of different polymorphisms,respectively 12 and 15, strongly suggests that these SPCs arose beforethe split of the species several hundred thousand years ago [Tiffin andGaut, Genetics 158: 401-412, 2001], and were maintained independently inthe two species.

It is anticipated that this type of analysis of SPC structures insequences from related species will have various practical utilities.First, the identification of SPCs that are shared between species mayserve as a useful criterion for identifying SPCs that could befunctionally important. The rationale is that SPCs that have beenretained in different species may represent alleles that one way oranother confers selective advantage and hence may represent alleles withdistinct functional properties. As most of the genomes of species ofagricultural importance will become sequenced in the near future, it isanticipated that comparative sequencing of genes or even entire genomesof related species will become routine. In this future perspective, themethods of the present invention will provide a most valuable tool fortargeting functionally important alleles of genes that are important foragricultural performance. Second, the comparative analysis of SPCs inloci from large numbers of different accessions of closely relatedspecies provides a logical framework for a rational approach forexploiting the genetic diversity in related species. It is projectedthat in the future the broadening of the genetic diversity of commercialgermplasm in plant and animal breeding through interspecific crosseswill become a major source of genetic innovation and improvement. Thisis now well documented in for example tomato. The problem however todayis that we have no means for selecting appropriate accessions, nor do wehave a valid means to evaluate or appreciate the genetic diversitypresent in accessions. The methods of the present invention provide ameans to rationalize the structure of interspecies genetic diversity andto select the most appropriate accessions for interbreeding. Forexample, based on the SPC structures observed at a number of differentloci, one can choose accessions that exhibit high frequencies of novelSPCs at various loci to broaden the basis of genetic variation availablefor genetic selection. Thus the method of the present invention providesa superior method of monitoring genetic diversity in wild accessions ofthe species and related species.

In conclusion, this example shows that the interspecific SPC maps of alocus can provide insights into the complex phylogenetic origins ofgenetic variation. When the same SPC is found in different species, thenit is likely that the mutations that make up this SPC arose before thesplit of the species, whereas SPCs that are unique to one speciespresumably arose after the speciation event. It is noted that theextremely high variation found in the globulin 1 gene presumably resultsin a large number of recurrent mutations confounding the precisephylogeny.

EXAMPLE 5 SPC Map of the FRI Locus of Arabidopsis thialiana

The present example provides proof of concept that that the methods ofthe present invention can be used to construct SPC maps of entiregenomic segments, covering large numbers of genes. Examples 1 through 3illustrated that the analysis of gene loci with the methods of thepresent invention may yield different types of SPC maps depending uponthe recombination history of the locus. This example presents ananalysis of the polymorphic sites in the genomic region surrounding theFRI locus of Arabidopsis thaliana to provide proof of concept that SPCmaps can also generated for genomic regions comprising many genes usingpolymorphism data sampled throughout a genomic region. One approach forassessing allelic diversity in genomic regions that is becoming widelyused involves the sequencing of short segments (500 to 1000 bp, thelength of a typical sequence run) from different places throughout thegenomic region of interest. Several studies of this type have beenpublished recently, and one of these was chosen in the present example.

The genomic sequences analyzed in the present example were generated inthe study of a 450-kb genomic region surrounding the flowering timelocus FRI [Hagenblad and Nordborg, Genetics. 161: 289-298, 2002] andcomprises a set of 14 amplicons sequenced from 20 accessions ofArabidopsis thaliana.

The sequences for this analysis were retrieved from GenBank(http://www.ncbi.nlm.nih.gov) accession numbers AY092417-AY092756. Theindividual sequences were aligned to generate 14 genetic variationtables as described in detail in Example 1, which were subsequentlycombined into a single continuous genetic variation table. The geneticvariation table of the FRI locus comprises 191 polymorphic sites. TheSPCs that comprise 3 or more polymorphic sites were computed with theSPC algorithm using the following thresholds: C=1 and C≧0.75. Thealgorithm clustered respectively 85 and 94 polymorphisms at clusteringthresholds of C=1 and C≧0.75.

FIG. 13A shows a physical map of the 450-kb region surrounding theflowering time locus FRI, and FIG. 13B shows the SPC map of the regionobtained using the C≧0.75 threshold. For the sake of clarity, SPCs ofsingletons (40 out of 94 clustered polymorphisms) are not displayed. Itcan be seen that several SPCs extend over a part of the region, whileothers are confined to short segments. This example illustrates that inlarger genomic regions where the frequency of recombination is low, someof the SPCs can extend over long distances. This is one of the principaldistinctions between the method of the present invention and thehaplotype block method. The haplotype block method will divide genomicregions into blocks according to observed recombination events, using acertain threshold. The method of the present invention will detectrecombination events in the SPCs that are affected, but these will notaffect the other SPCs. The results presented in the present exampledemonstrate that the SPC method is superior in capturing the structurein the genetic variation.

EXAMPLE 6 SPC Maps of Surveys of Genetic Diversity in Arabidopsisthaliana

The present example provides proof of concept that that the methods ofthe present invention can be used to construct SPC maps of entiregenomes from genome-wide genetic diversity data, and that from the SPCmap ctSNP markers can be derived for genome-wide association studies.Several approaches for surveying genetic diversity on a genome-widescale are currently being pioneered, involving sequencing shortfragments of 500 to 1000 bp amplified from genomic DNA from a collectionof individuals representative for the species. In one approach theamplicons are chosen at regular intervals (20 or 50 kb) along thegenome, while other approaches rely on the systematic sequencing ofregions of known genes. This example presents an analysis of thepolymorphic sites identified in a set of amplified fragments fromchromosome 1 of Arabidopsis thaliana.

The genomic sequences analyzed in the present example were generated inthe NSF 2010 Project “A genomic survey of polymorphism and linkagedisequilibrium in Arabidopsis thaliana” [Bergelson J., Kreitman M., andNordborg M., http://walnut.usc.edu/2010/2010.html] and comprises 255amplicons from chromosome 1 sequenced from 98 accessions of Arabidopsisthaliana.

The sequences for this analysis were downloaded from the websitehttp://walnut.usc.edu/2010/2010.html. The individual sequences werealigned to generate one genetic variation table per amplicon asdescribed in detail in Example 1. Singletons and polymorphic sites withmore than 33% missing data were excluded from the analysis. Theindividual tables were concatenated into a single genetic variationtable in the same order in which the amplicons occur on the chromosome.The resulting genetic variation table of chromosome 1 contains 3378polymorphic sites. The genetic variation table was analyzed with the SPCalgorithm using a sliding window of 120 polymorphic sites and an overlapof 20 SNPs between each consecutive block. The following parametersettings were used in this analysis. First, since the genetic variationtable contains a substantial number of missing data points (6.5%) theallele and two-site haplotype frequencies were calculated by the ratioof the observed number of alleles/haplotypes over the total number ofsamples minus the number of missing data points. Second, all SPCs ofthree or more polymorphisms were identified using the followingthresholds for C: C=1, C≧0.90 and C≧0.80.

Analysis of the global results for chromosome 1 revealed that ˜60% ofthe amplicons yielded one or more SPCs containing at least 3polymorphisms at the threshold of C≧0.90. FIG. 14 shows the SPCsidentified in 31 amplicons (from amplicon #134 to amplicon #165) from a3.76 Mb segment of chromosome 1 (from position 16,157,725 to position19,926,877). It can be seen that the amplicons that do not yield SPCs(10 of the amplicons of FIG. 14) generally have relatively fewpolymorphic sites, although occasionally amplicons are observed thathave numerous polymorphisms that fail to cluster (e.g. amplicons 144 and147). The amplicons yielding SPCs were broadly classified into 2classes, each occurring with similar frequency. The class I ampliconsreveal only one SPC (e.g. amplicons 142, 150, 152, 153, 154, 155 and158). The class II amplicons reveal two or more overlapping SPCs (e.g.amplicons 136, 137, 139, 143, 145, 146, 148 and 163). The class Iamplicons correspond to dimorphic loci, i.e. loci that have only twohaplotypes (SPC-n and SPC-0), while the class II amplicons correspond topolymorphic loci, i.e. loci that have three or more haplotypes. Whilethe polymorphic loci obviously reflect a greater genetic diversity, itcan be seen from FIG. 14 that the number of SPCs observed in the classII amplicons is fairly small, mostly two or three and occasionally more.Finally it can be seen from FIG. 14 that nearly all the SPCs found areconfined to a single amplicon, with three exceptions denoted by theblack arrows. In each case it is a single polymorphic site in anadjacent amplicon that is included in the cluster. Since the averagedistance between the amplicons is in the order of 100 kilobases, theobservation that the SPC structures are amplicon-specific indicates thatthe long range LD in Arabidopsis is less then 100 kilobases. It istherefore anticipated that a much higher density of sequences must besurveyed to construct an SPC map of this organism.

In conclusion, this example demonstrates that the SPC method is wellsuited to assess the genetic diversity at both the level of an entiregenome. Moreover, the discovered SPC structures provide a logicalframework for the development of useful sets of DNA markers for geneticanalysis of a species. For each SPC only one representative ctSNP ischosen. This marker set will be universally applicable in the species.

This present method of analyzing genetic diversity has usefulapplications in plant and animal breeding, in that it provides both ameans to develop useful genetic markers, as well as allowing breeders toselect appropriate lines for introducing new genetic diversity inbreeding programmes. Based on the SPCs found, one can develop SPC tagswhich can be used for both identifying genes involved in agronomicaltraits and for marker assisted breeding. The SPC maps are useful foridentifying lines that carry novel SPCs that are not present in thebreeding germplasm and that can provide novel genetic diversity.

EXAMPLE 7 SPC Map of the Human CYP4A11 Gene

The present example provides proof of concept that the methods of thepresent invention can be used on unphased diploid genotype data both toconstruct an SPC map of a gene and to select tag SNPs for geneticanalysis. The present example will also provide proof of concept thatthe methods of the present invention can be used to infer haplotypesfrom the unphased diploid genotypes. This example presents an analysisof the polymorphic sites in the human CYP4A11 (cytochrome P450, family4, subfamily A, polypeptide 11) gene to exemplify the different aspectsof the invention. The genetic variation data analyzed in the presentexample was generated by the UW-FHCRC Variation Discovery Resource[SeattleSNPs; http://pga.gs.washington.edu/]. The UW-FHCRC VariationDiscovery Resource (SeattleSNPs) is a collaboration between theUniversity of Washington and the Fred Hutchinson Cancer Research Centerand is one of the Programs for Genomic Applications (PGAs) funded by theNational Heart, Lung, and Blood Institute (NHLBI). The goal ofSeattleSNPs is to discover and model the associations between singlenucleotide sequence differences in the genes and pathways that underlieinflammatory responses in humans.

The unphased diploid genotypes and the SNP allele data tables for thisanalysis were downloaded from the SeattleSNPs website(http://pga.gs.washington.edu/). The genetic variation data for theCYP4A11 gene comprise 103 polymorphic sites (SNPs and indels) that wereidentified by resequencing a segment of 13 kb in 24 African American and23 European individuals. The diploid genotype data table lists theallele scores of the 103 polymorphic sites of the CYP4A11 gene in the 47samples. The diploid genotype data table was first reformatted to thestandard format for genetic variation tables as described in Example 1using the following procedure. Homozygous diploid SNP genotypes weredenoted by the symbols “A”, “C”, “G” or “T”, while homozygous indelgenotypes were denoted by a dot for the deletion allele or,alternatively, the first base of the insertion. The heterozygous diploidgenotypes (polymorphic sites at which both alleles were scored) weredenoted by the symbol “H”. Thereafter a table of artificial haplotypes,termed metatypes, was derived from the genetic variation table using thefollowing procedure. The table was first duplicated by adding a secondcopy of the sample rows. Thereafter the symbols “H” were replaced ineach of the two copies respectively by the minor allele in the firstcopy and by the major allele in the second copy. The duplicated andreformatted genetic variation table is referred to as the metatypetable. The diploid genotypes in which the symbols “H” were replaced bythe minor allele are referred to as minor metatypes and the diploidgenotypes in which the symbols “H” were replaced by the major allele arereferred to as major metatypes. The sample names in the metatype tableare denoted with the extension “−1” for the minor metatypes, and withthe extension “−2” for the major metatypes. It is noted that twoessential features of the polymorphic sites are perfectly retained inthe metatype format, namely the frequencies of the alleles and theirco-occurrence or linkage. Indeed, each diploid genotype is disassembledin two metatypes, and each heterozygous genotype is correctly split intoone minor and one major allele in the two metatypes. The linkagesbetween the co-occurring polymorphic sites are retained by thesimultaneous replacement of all heterozygous genotypes on a singlediploid genotype by either the minor or the major alleles inrespectively the minor and major metatypes.

The metatype table was analyzed with the SPC algorithm using thefollowing parameter settings. First, since the metatype table contains asubstantial number of missing data points, “N”, (3.8%) the allele andtwo-site haplotype frequencies were calculated by the ratio of theobserved number of alleles/haplotypes over the total number of samplesminus the number of missing data points. Second, all SPCs of two or morepolymorphisms were identified using the following thresholds for C: C=1,C≧0.95 C≧0.90, C≧0.85 and C≧0.80.

The SPC algorithm clustered the majority of the 103 polymorphic sites atthe different thresholds: 69 (67%), 81 (79%) and 84 (82%) polymorphicsites at respectively C=1, C≧0.90 and C≧0.80. The polymorphisms were formost part clustered in similar SPCs at the different thresholds, withtwo exceptions. The polymorphisms of SPC-2 were clustered in twodifferent SPCs at the threshold of C=1, which became merged into SPC-2at the threshold of C≧0.90. SPC-14 was found only at the threshold ofC≧0.80. In the section below the SPC map of the 81 polymorphic sitesclustered at the threshold of C≧0.90 is analyzed in detail, thusexcluding SPC-14.

In FIG. 15A the 13 different SPCs clustered at the threshold of C≧0.90,comprising 81 polymorphisms, are visualized onto the metatypes. In theupper half of FIG. 15A the SPCs found in the major metatypes (samplename followed by “−2”) are shown, while the lower half of FIG. 15A showsthe SPCs observed in the minor metatypes (sample name followed by “−1”).The 69 polymorphisms that were clustered at the threshold of C=1 arehighlighted in the upper row of FIG. 15A. Only those metatypes that docontain one or more SPCs (comprising minor alleles) are listed. Themetatypes that are devoid of an SPC (SPC-0) are omitted, except for onerepresentative in each table half. The minor and major metatypes weresorted according to the SPCs present. A striking feature of FIG. 15A isthat SPC-2 is present in all metatypes that are not SPC-0, either aloneor in combination with other SPCs. This observation suggests that many(if not all) SPCs are dependent on SPC-2.

The relationships between the SPCs were inferred in a two step process:first, the SPC combinations observed in the major metatypes wereexamined; second, the SPCs observed in the minor metatypes weresystematically compared to the SPCs observed in the corresponding majormetatypes. This comparison between the major and minor metatypes isillustrated in FIG. 15B. Examination of the SPCs found in the majormetatypes (upper panel of FIG. 15A) reveals that (1) SPC-13 isinvariably found in combination with SPC-2, but not vice versa, while(2) SPC-1 and SPC-4 each appear on a fraction of the metatypes thatcontain both SPC-2 and SPC-13. It follows from these observations thatSPC-1 and SPC-4 depend on SPC-13, which in turn depends on SPC-2.

For the comparison between the major and minor metatypes shown in FIG.15B, the subgroup of representative metatypes was arranged into threeseparate classes. Class I, shown in the upper panel of FIG. 15B,represents those metatypes that exhibit identical SPCs in both the minorand the major metatype. Class II, shown in the middle panel of FIG. 15B,represents those metatypes that exhibit different SPCs in the minor andthe major metatype. Class III, shown in the lower panel of FIG. 15B,represents those minor metatypes for which the major metatype exhibitsSPC-0. The class I metatypes reveal two SPC combinations: 1-2-13 and2-4-13, consistent with the dependency of SPC-1, SPC-4 and SPC-13 onSPC-2. Analysis of the class II metatypes reveals that the minormetatypes which exhibit pairwise combinations of the SPCs 1, 3, 4, 5 and7 all have a major metatype that exhibits SPC-2 (and often also SPC-13).This pattern is consistent with a relationship in which each of theseSPCs is independent from one another and dependent on SPC-2 (either withor without SPC-13 as an intermediate). For example, the minor metatypeD009-1 has the SPCs 1, 2, 3 and 13 and its major metatype has theSPC-2/SPC-13 couple, showing that both SPC-1 and SPC-3 are dependent onSPC-13, and the higher ranked SPC-2. The same logic applies to D005,leading to the conclusion that SPC-1, SPC-3 and SPC-5 are all mutuallyindependent and that each depends on, sequentially, SPC-13 and SPC-2.Inspection of the sample D039 and D040, in which case the majormetatypes only contain SPC-2, point to a first-degree dependence ofSPC-4 and SPC-7 on SPC-2. According to the foregoing reasoning SPC-4 isobserved both in direct dependency on SPC-2 as well as through theintermediate SPC-13; this apparent conflict in the relationship can beattributed to a historic recombination event. D007 and E016 are therecombinant samples that cause the dual observation (see FIG. 15A).Further analysis along the same line suggests that the SPC-9 and SPC-12are also dependent on SPC-13, but it cannot be firmly concluded from thesingle observation in sample D015 whether SPC-9 and SCP-12 are in anindependent or a dependent relationship with respect to each other.Finally, SPC-11 is observed once in a minor metatype that has also SPC-3and SPC-5 (sample D010), indicating that SPC-11 must be dependent on oneof them. Apart from the supplementary inference that SPC-12 cannotdepend on SPC-9, the analysis of the class III metatypes only serves toconfirm the above dependencies. In general class III metatypes do notprovide additional information because the major metatypes are notinformative. Hence, the dependencies of SPCs 6, 8 and 10, which areobserved in one sample only, cannot be established. For example, theminor metatype D036-1 has the SPCs 2, 3, 10 and 13 and its majormetatype has SPC-0. Apart from knowing the dependency rank of SPC-2,SPC-3 and SPC-13, one cannot unambiguously assign SPC-10: SPC-10 couldbe dependent on SPC-3 but could also be dependent on SPC-0. Inconclusion, the analysis of the metatypes shows that of the 13 SPCsidentified in the CYP4A11 gene, the dependencies of 9 of them could beestablished through logic inference from the SPC patterns observed inthe metatypes. FIG. 15C shows a visual representation of the network ofhierarchical relationships established between the 9 SPCs in the CYP4A11gene.

In conclusion the above analysis demonstrates that the methods of thepresent invention can be used to cluster the polymorphic sites into SPCsstarting from unphased diploid genotypes. The SPCs patterns observed inthe minor and major metatypes, allows the deduction of the hierarchicalrelationships between most of the SPCs found. The analysis demonstratesthat the inferred relationships between SPC-1, SPC-2, SPC-3, SPC-4,SPC-5, SPC-7, SPC-12 and SPC-13 are firmly established since they arebased on multiple and complementary observations, but that certainrelationships remain speculative because of insufficient observations(e.g. SPC-9). In the present study, we have assumed that SPC-9 isdirectly dependent from SPC-13 and we included SPC-9 in the furtheranalysis. Together these 9 SPCs account for 67 of the 81 clusteredpolymorphic sites. It should be noted that the SPCs whose relationshipcannot be firmly established all have a low occurrence frequency: SPC-6(occurs twice and consists of 6 SNPs), SPC-8 (singleton, 4 SNPs), SPC-10(singleton, two polymorphisms), SPC-11 (singleton, 2 SNPs), and SPC-9(singleton, 3 SNPs). It is anticipated that the analysis of additionalsamples would enable the establishment of the relationships of theseSPCs. Indeed, the skilled person will realize that the outcome of theabove analysis is determined primarily by the number of informativeobservations, and that the remaining ambiguity is not related toinherent limitation of the method.

Based on the established relationships between the 9 SPCs, the SPCs cannow be mapped unambiguously. The SPC map presented in FIG. 15D shows inthe upper panel the inferred haplotypes onto which the different SPCcombinations observed in the metatypes are visualized, and the lowerpanel shows the 67 polymorphic sites that are clustered in each of the 9SPCs. The 9 SPCs are organized in a total of only 10 inferred haplotypesdesignated by the SPC combinations present: 2-13, 2-1-13; 2-3-13; 2-4;2-4-13; 2-5-13; 2-7; 2-9-13; 2-12-13 and 0 (the haplotype that has noSPC). It is noted that while all 10 inferred haplotypes were found inAfrican American individuals only three of them were observed inEuropean individuals (2-1-13; 2-4 and 2-4-13). This is in good agreementwith earlier findings that Europeans carry only a subset of thehaplotypes found in Africans.

The inferred haplotypes can now be used to deconvolute the diploidgenotypes, as shown in the last two columns of FIG. 15B. The rationalefor the deconvolution is that the minor metatypes represent combinationsof two of the inferred haplotypes, and that the major metatypesrepresent those SPCs that are common between the two inferredhaplotypes. The grouping of the metatypes into three classes (see FIG.15B) is also useful for the deconvolution. The class I metatypes haveidentical SPC combinations in both minor and major metatype, and theseSPC combinations are also found among the inferred haplotypes.Consequently the class I metatypes are simply deconvoluted into twoidentical haplotypes. For example, sample E012 which has the SPCcombination 1-2-13 is deconvoluted into two 1-2-13 haplotypes. The classII metatypes display different SPC combinations in the minor and majormetatypes. Each minor metatype must represent a combination of twoinferred haplotypes other than “0”, and which share the SPCs representedin the major metatype. For example, sample D009 which has in the minormetatype the SPC combination 1-2-3-13 and 2-13 in the major metatype isdeconvoluted into the two haplotypes 1-2-13 and 2-3-13. The class IIImetatypes display SPC combinations in the minor metatypes and no SPCs inthe major metatypes. Each minor metatype must thus represent acombination of two inferred haplotypes which share no SPCs. Since allthe SPCs are dependent on SPC-2, one of the haplotypes must be “0”. Forexample, sample E019 which has in the minor metatype the SPC combination1-2-13 is deconvoluted into the two haplotypes 1-2-13 and 0.

In conclusion the above analysis demonstrates that the methods of thepresent invention can be used for correct inference of haplotypes fromunphased diploid genotype data.

Finally it is demonstrated that the unphased diploid data that were usedto compute the SPCs can also be used to select ctSNPs for geneticanalysis, without the need for prior haplotype inference. The presentinvention provides a means to select those polymorphic sites that mostclosely match the SPC and are thus most suited to serve as ctSNPs. Themethod is based on a calculation of the average linkage value (AVL) ofeach polymorphism with all other polymorphisms of the SPC. As explainedherein above, this calculation not only considers aberrant data (i.e.the minor alleles are not present in all samples carrying the SPC or arefound in other samples) but also take missing genotypes into account toevaluate the suitability of SNPs. In the present example, the selectionof ctSNPs is illustrated in FIGS. 15E, F and G for three SPCs,respectively SPC-1, SPC-2 and SPC-4. These Figures show the matrices ofpairwise linkage values together with the metatypes of the polymorphicsites for each SPC. FIG. 15E shows the selection of ctSNPs for SPC-1.The two equivalent ctSNPs of choice, characterized by the largest ALVvalues, are SNP-33 and SNP-45. Both SNPs best represent the SPC becausethe minor alleles are found in all samples carrying the SPC and do notoccur in other samples while, additionally, there are no missing datapoints. The next best tags also perfectly match with the SPC, but dohave missing data in the remainder of the samples. FIG. 15F shows theselection of ctSNPs for SPC-2. Here again, the two SNPs that have thelargest ALV values, SNP-31 and SNP-40 both perfectly match with the SPCwithout missing data points. All other SNPs have either missing datapoints or exhibit aberrant scores. FIG. 15G shows the selection of tagSNPs for SPC 4. Finally, it is noted that when there are no-aberrant ormissing data points for the clustered polymorphic sites, i.e. when allpolymorphic sites are clustered at the threshold of C=1, all sites areequivalent, and consequently each of them can serve as ctSNP.

EXAMPLE 8 SPC Map of a Class II Region of the Human MHC Locus

The present example provides further proof of concept that the methodsof the present invention can be used on unphased diploid genotype datato construct SPC maps of complex genomic loci and to select ctSNPs fordeveloping diagnostic markers for genetic analysis. The present examplealso provides proof of concept that the methods of the present inventioncan be used to analyze loci in the human genome exhibiting complexpatterns of recombination. This example presents an analysis ofpolymorphic sites in the human major histocompatibility complex (MHC)locus. The MHC locus is known to exhibit complex patterns of geneticvariation and is currently the focus of intensive genetic researchbecause of its importance in many human diseases. The MHC locus is alsoone of the few loci in the human genome in which the existence ofrecombinational hotspots is well documented, and the present examplecomprises a 216-kb segment of the class II region of the MHC in whichdifferent recombinational hotspots have been mapped with great precision[Jeffreys et al., Nat. Genet. 29: 217-222, 2001].

The diploid genotypes and the SNP allele data for the “SNP genotypesfrom upstream of the HLA-DNA gene to the TAP2 gene in the Class IIregion of the MHC” [Jeffreys et al, Nat. Genet. 29: 217-222, 2001] werecopied from the websitehttp://www.le.ac.uk/genetics/ajj/HLA/Genotype.html. The data comprise296 SNPs typed in a panel of 50 unrelated UK Caucasian semen donorsusing allele-specific oligonucleotide hybridisation of genomic PCRproducts. The diploid genotype table lists the allele scores of the 296polymorphic sites of the class II region of the MHC in the 50 samples.This table was reformatted into a metatype table exactly as described inExample 7 with the following minor modifications: single baseinsertion/deletion genotypes (denoted as ±),were replaced by the symbol“A” or a dot, respectively, while the missing genotypes (denoted by “?”or “.”) were converted into the symbol “N”.

The metatype table was analyzed with the SPC algorithm using the sameparameter settings as in Example 7, with the following thresholds for C:C=1, C≧0.95, C≧0.90, C≧0.85 and C≧0.80. At the C≧0.80 threshold, the SPCalgorithm clustered 198 of the 296 polymorphisms into 40 different SPCs.The pattern of SPCs is shown in FIG. 16B and 16C. Note that, in order toreduce the size of the Figure, the analysis was performed on twoseparate sets of SNPs, more specifically the subgroup of SNPs with highfrequency minor alleles (observed more than 8 times or >16%; FIG. 16B)and the SNPs characterized by low frequency minor alleles (≦16%; FIG.16C). The SNPs in each subgroup cluster into 20 SPCs. FIG. 16B/C clearlyshows that nearly all of the SPCs are confined to 7 different domainswithin the 216-kb segment; these domains are represented by thedifferently highlighted rectangles that refer to the physical map shownin FIG. 16A. Overall, each domain comprises a different set of SPCs andthere are (almost) no SPCs that extend into adjacent domains. This isconsistent with the presence of recombination hotspots between thedomains that have disrupted the SPCs. Indeed, the domain boundariespredicted by the SPC map correspond very well with the positions of therecombination hotspots which were identified by Jeffreys and co-workers,and which are indicated by the red arrows in FIG. 16A. Furtherinspection of FIG. 16B/C shows that there are a few exceptional SPCsthat are spanning multiple domains, most notably SPC-2 and SPC-7 thatare indicated by heavy arrows in FIG. 16C. SPC-2 is found in domains 1,3 and 6 and comprises singleton SNPs observed in one sample. The otherSPC, SPC-7, occurs in domains 4 and 7 and is observed in. eightindividuals. These results illustrate an important difference betweenthe SPC and the haplotype block concepts: irrespective of the incidenceof recombination, the integrity of certain SPC is unaffected (i.e. theassociation of certain polymorphisms, belonging to different blocks,remains intact) resulting ultimately in the selection of a smaller setof tag SNPs. The present example provides a clear illustration that theSPC patterns in regions that have long history of recombination canreadily be obtained from unphased diploid genotype data.

Once the domain structure of a genomic region under investigation isestablished, it is then possible to determine the hierarchicalrelationships between the SPCs in each domain. Once the SPC structure ofa genomic region under investigation is established, it is then possibleto determine the hierarchical relationships between the SPCs. This isillustrated for the SNPs of domain 4 in FIG. 16A. This domain comprises67 SNPs between positions 35.095 and 89.298. In this analysis the subsetof 57 SNPs with a minor allele frequency of 5% or more were selected.The metatype table for the 57 SNPs was reanalyzed with the SPC algorithmusing the same parameter settings as above. In total 52 of the 57 SNPswere clustered in 9 SPCs. The relationships between the SPCs are shownin the network structure of FIG. 16E; they were inferred by comparingthe SPCs found in the minor metatypes and their corresponding majormetatypes as outlined in detail in Example 7. The analysis revealed thatthe SPCs are organized in 8 SPC-haplotypes (including the haplotype thatis devoid of SPCs) as shown in the SPC map in FIG. 16D. In essence allof the metatypes were consistent with the deduced SPC-haplotypes oroccasional recombinants between these. Tag SNPs (ctSNPs) that bestrepresent the various clusters can obviously be selected in the absenceof an SPC map and accompanying network structure. However, in caseswhere the network is multi-layered and shows many levels of dependency,as in the present example, it provides a rational basis to furtherreduce the number of tag SNPs. For instance, it is possible to restrictan analysis to tag SNPs that are specific for SPCs that are high up inthe hierarchy (i.e. that are clade-specific).

It should be noted that in comparison with the SPC map of the CYP4A11locus described in Example 7, the SPC map of the MHC locus is much morecomplex. This is consistent with the much higher genetic variability ofthe MHC locus. It can be anticipated that the SPC-haplotypes describedin the present example represent only a fraction of those that may beuncovered in the human population. Indeed the data analyzed here werefrom a limited population sample of North Europeans. Hence the SPCmapping strategy provides a useful method to analyze the organizationalpatterns of SNPs and to design reliable tag SNPs for genetic resting.

EXAMPLE 9 SPC Map of HapMap SNPs of Human Chromosome 22

The present example provides further proof of concept that the methodsof the present invention can be used on unphased diploid genotype datato construct SPC maps of the human genome and that the SPC maps areparticularly useful for selecting ctSNPs as diagnostic markers forgenome-wide genetic association studies. This example presents ananalysis of the genetic variation data recently generated in theInternational human HapMap project (The International HapMap Consortium,Nature 426: 789-796, 2003) to exemplify the different aspects of theinvention. The aim of the International HapMap Project is to determinethe common patterns of DNA sequence variation in the human genome, bycharacterizing sequence variants, their frequencies, and correlationsbetween them, in DNA samples from populations with ancestry from partsof Africa, Asia and Europe. The project will provide tools that willallow the indirect association approach to be applied readily to anyfunctional candidate gene in the genome, to any region suggested byfamily-based linkage analysis, or ultimately to the whole genome forscans for disease risk factors.

The unphased diploid genotypes and the SNP allele data of public datarelease #3 for chromosome 22 was downloaded from the HapMap websitehttp://www.hapmap.org/ (The International HapMap Consortium, Nature 426:789-796, 2003). Chromosome 22 was chosen for this analysis because ofthe relatively high density of SNPs genotyped on this chromosome,averaging 1 SNP per ˜5 kb. The unphased diploid genotypes list the SNPallele scores of the 5865 polymorphic sites of chromosome 22, genotypedin 30 father-mother-child CEPH trios and 5 duplicate samples (95individuals in total). The chromosomal positions of each SNP are givenin basepairs on reference sequence “ncbi_b34”. A genetic variation tablewas derived from the unphased diploid genotypes by converting thehomozygous genotypes denoted by two identical symbols (e.g. “AA”) intosingle letter symbols (e.g. “A”) and the heterozygous genotypes denotedby two different symbols (e.g. “AG”) into the symbol “H”. Missinggenotypes are represented by the symbol “N”. The genetic variation tableof chromosome 22 was divided into consecutive blocks of 120 SNPs with anoverlap of 20 SNPs between each consecutive block. Finally, areformatting into consecutive tables of metatypes was performed asdescribed in Example 7.

The metatype table was analyzed with the SPC algorithm with the sameparameter settings as in Example 7.The present Example is directed atthe analysis of a segment of 2.27 Mb comprising 700 SNPs, correspondingto an average of 1 SNP per 3.24 kb. The SPC algorithm clustered asubstantial fraction of the SNPs at the different thresholds:respectively 48%, 66% and 74% at the thresholds of C=1, C≧0.90 andC≧0.80. As can be seen from the SPC map obtained at a clusteringthreshold of C≧0.90 shown in FIG. 17B, roughly half of the SNPs wereclustered in domains exhibiting extensive and interspersed SPC patterns,while the other half of the SNPs yielded mostly short isolated SPCscomprising a few SNPs. In total 11 domains comprising 10 or moreclustered SNPs were identified; the domains are drawn to scale on thephysical map shown in FIG. 17A. These 11 domains represent 785 kb or˜35% of the 2.27 Mb segment. While most domains are between 25 kb and 50kb, the 4 largest domains span 100 to 200 kb and comprise 45 to 65 SNPs.It is noted that the SPCs are separated by stretches of SNPs that do notcluster, not even at low thresholds.

These results from a small sample of the HapMap data demonstrate thatthe methods of the present invention are capable of capturing the SPCstructure in the unphased diploid HapMap genotype data, and provide arobust approach for the identification of domains of extensive haplotypestructure. It can be anticipated that a much more extensive SPCstructure will be uncovered as the density of the SNPs genotyped in theproject increases. At the same time, one can also expect that in certainregions of the genome the SPC structure will remain highly fragmented asa result of extensive recombination. These may correspond to the regionsin which little or no SPC structure is observed in the present release.Based on the SPCs found in the HapMap data, the methods of the presentinvention may furthermore be used for the selection of tag SNPs(ctSNPs). Such ctSNPs can be selected both in the less structuredregions and in the domains of extensive SPC structure. When genotypesfor additional SNPs become available in the future, this list can simplybe updated by adding tag SNPs for the novel SPCs that will be uncovered.It should be stressed that the tag SNPs that are identified on the basisof the current analysis will, in general, remain valid in the future.

Domain 9 of FIG. 17B was analyzed in detail to exemplify one of theaspects of the present invention, more specifically the ability toidentify potentially erroneous genotype data that one may want to verifyexperimentally. Domain 9 comprises 59 SNPs of which 58 are clustered in6 SPCs at a threshold of C≧0.90. The relationships between 5 of the 6SPCs, shown in the network structure of FIG. 17D, were inferred bycomparing the SPCs found in the minor metatypes and their correspondingmajor metatypes as outlined in detail in Example 7. The sixth SPCcomprises 3 singleton SNPs observed in one sample that was excluded fromthe analysis. The deconvolution analysis revealed that the SPCs areorganized in 6 SPC-haplotypes (including the haplotype that is devoid ofSPCs) as shown in the SPC map in FIG. 17C. Apart from the aberrantsample, all 89 metatypes were consistent with the 6 SPC-haplotypes oroccasional recombinants between these. The SNP genotypes that wereinconsistent with the SPC map were examined in detail. An inconsistencyconsists of either the absence of a SNP minor allele in metatypes thatcontain the SPC to which the SNP belongs, or, alternatively, thepresence of a minor allele in a metatype that does not carry the SPC. Intotal 15 of the 5220 SNP genotypes (58 SNPs×30 trios) were observed thatwere inconsistent with the SPC structure (<0.3%). Of these, 6 genotypescould be classified as genotyping errors because of discrepanciesbetween the genotype of the parents and that of the child. This isillustrated in FIG. 17E which represents the metatypes of 3 trios(parents and child) with their corresponding SPC-haplotypes. In thefirst trio (upper panel of FIG. 17E) the minor allele of SNP-24(belonging to SPC-1) is genotyped in one of the parents, but not in thechild. In the second trio (middle panel of FIG. 17E) the minor allele ofSNP-39 (belonging to SPC-1) was not genotyped in the child, whichinherited one copy of SPC-1 from each parent. In the third trio (lowerpanel of FIG. 17E) the minor allele of SNP-30 (belonging to SPC-1) wasgenotyped in the child, while SPC-1 is not present in either parent. Inthe last two cases the genotyping error is evident, while it is likelyin the first case. This finding highlights another aspect of the presentinvention, namely the identification of potentially incorrect genotypesbased on inconsistencies with the SPC structure.

EXAMPLE 10 SPC Map of 500 Kilobases on Chromosome 5q31

The present example provides an illustration of the differences betweenthe SPC maps constructed with the methods of the present invention andthe haplotype blocks obtained with the approach proposed by Daly et al.[Daly et al., Nat. Genet. 29: 229-232, 2001; Daly et al., patentapplication US 2003/0170665 A1]. The present example also provides anillustration of the differences between the tag SNPs (ctSNPs) selectedwith the methods of the present invention and the haplotype tag SNPs(htSNPs) selected with the haplotype block method. This example presentsa reanalysis of the polymorphic sites in a 500 kb segment on chromosome5q31, which had been used to establish the presence of haplotype blocksin the human genome [Daly et al., Nat. Genet. 29: 229-232, 2001]. Theresults of the analysis presented provides evidence that the ctSNPsselected with the methods of the present invention are superiordiagnostic markers for genome wide genetic association studies, andgenetic analysis in general.

The unphased diploid genotypes and the SNP allele data for the“High-resolution haplotype structure in the human genome” [Daly et al.,Nat. Genet. 29: 229-232, 2001] were downloaded as “Download raw-datapage” from the websitehttp://www.broad.mit.edu/humgen/IBD5/haplodata.html. The data of the 500kb segment on chromosome 5q31 comprise 103 SNPs typed in a panel of 129trios, amounting to 387 individuals. The raw-data page lists numericalsymbols representing the alleles of the 103 polymorphic sites genotypedin the 387 samples. The numerical symbols were replaced by the symbols“A”, “C”, “G” and “T” for the homozygous genotypes and by the symbol “H”and “N” for respectively the heterozygous genotypes and the missinggenotypes. The genetic variation table was reformatted into a metatypetable as described in Example 7.

The metatype table was analyzed with the SPC algorithm using thefollowing thresholds for C: C=1, C≧0.95, C≧0.90, C≧0.875, C≧0.85 andC≧0.825. The analysis of the present data set was encumbered by thelarge number of missing data points (i.e. 10.4%) combined with therelatively high incidence of recombination. The SPC pattern that wasultimately assembled gathers information about the clustering atdifferent stringencies. Basically, the 15 SPCs that were identified atthe C≧0.875 threshold were retained and SNPs that clustered at the lowerthresholds were added (without allowing the SPCs themselves tocoalesce). In total 87 of the 103 SNPs were clustered.

FIG. 18 shows that the SPC pattern of the 103 SNPs is discontinuous atboth ends of the map (short alternating SPCs), while the central partcomprises long overlapping SPCs. The haplotype block structure [Daly etal., Nat. Genet. 29: 229-232, 2001] is represented by the numbered greyrectangles in FIG. 18. Comparison of the SPC pattern with the 11haplotype blocks shows that several SPCs are running across two or moreblocks, illustrating that the SPC structure provides a more conciserepresentation of the organization in the genetic variation. Theprincipal difference between the two methods lays in the selection oftag SNP markers for genotyping. In the haplotype block method tag SNPsare derived from the haplotypes identified within the blocks as SNPsthat are diagnostic for each haplotype, while the methods of the presentinvention define (at the most) one tag SNP for each SPC. Consequently,the SPCs that are spanning multiple adjacent blocks will be tagged morethan once, actually as many times as the number of blocks the SPC isencompassing. In contrast to the SPC concept, the consideration ofindependent blocks, leads a redundancy in the selection of markers. Inthe present example only 15 SNPs would be required for tagging the SPCswhile a comprehensive coverage of all block-specific haplotypes requireup to 37 htSNPs assuming one htSNP for each major haplotype within eachhaplotype block [refer to FIG. 2 in Daly et al., Nat. Genet. 29:229-232, 2001]. In addition, as documented in Example 7, the methods ofthe present invention provide a rational approach for selecting tag SNPsthat yield the most reliable marker for each SPC. A further primedifference between the SPC and the haplotype block concept, that is ofgreat practical utility, is that the SPC structure may be deriveddirectly from unphased diploid genotype data whereas the inference ofhaplotypes is a prerequisite for the haplotype block method.

EXAMPLE 11 SPC Map of Single-feature Polymorphisms in Yeast

The present example provides proof of concept that the methods of thepresent invention can be used on genetic variation data other thandefined sequence differences, and that the SPC maps thus obtained areparticularly useful for examining genome-wide patterns of geneticvariation. The present example provides this proof of concept forsingle-feature polymorphisms (SFPs) obtained using high-densityoligonucleotide arrays and demonstrates that the methods of the presentinvention can be used to design diagnostic microarrays that addressselected tag SFPs derived from the SPC maps. This example presents ananalysis of the polymorphic sites in chromosome 1 of common laboratorystrains of yeast identified using high-density oligonucleotide arrays[Winzeler et. al., Genetics. 163: 79-89, 2003]. In this study, theAffymetrix S98 oligonucleotide array (Affymetrix Inc, Santa Clara,Calif.) containing 285,156 different 25-mers from the yeast genomicsequence was used to discover 11,115 single-feature polymorphisms (SFPs)in 14 different yeast strains and to assess the genome-wide distributionof genetic variation in this yeast population. High-densityoligonucleotide arrays using short 25-mer oligonucleotides areparticularly useful for discovering polymorphisms because the strengthof the hybridisation signal can be used to detect nucleotide changes.Polymorphisms, detected through differential hybridisation to one singleoligonucleotide on an array (termed a feature) are referred to as“Single-Feature Polymorphisms” (SFPs). Thus, with oligonucleotide arrayscarrying large numbers of probes of this length, a substantialproportion of the genomic sequence can be interrogated and theapproximate position of allelic variation between two genomic sequencescan be ascertained. Microarrays of this type thus provide a powerfulplatform for genetic variation discovery and for future diagnosticgenotyping on a genome-wide scale.

The allelic variation data of intraspecies polymorphisms betweenlaboratory strains of yeast [Winzeler et. al., Genetics. 163: 79-89,2003] used in the present analysis were downloaded from the websitehttp://www.scripps.edu/cb/winzeler/genetics_supplement/supplement.htm.The allelic variation data table comprises the presence/absence scores(1/0) of 11,115 SFPs in 14 different yeast strains, together with theirposition on each of the 16 yeast chromosomes. The allelic variation datatable was converted into the standard format of the genetic variationtable by substituting the numerical symbols 0 and 1 by the symbols “C”and “A” respectively. The SFPs were sorted by chromosome and the geneticvariation table was partitioned into 16 tables comprising the SFPs ofindividual chromosomes. The genetic variation table of chromosome 1,analyzed in the present example, comprises 406 SFPs, of which 174 weresingletons. To simplify the analysis and the representation of theresults, the singletons were excluded from the analysis. The remaining232 polymorphisms were clustered with the SPC algorithm using thefollowing thresholds: C=1, C≧0.90 and C≧0.80. At the threshold of C=1and C≧0.90 the algorithm clustered a total of 117 SFPs (50%) ofchromosome 1 into 19 different SPCs comprising 3 or more SFPs. Therepresentation of FIG. 19 shows the chromosomal distribution of the SFPsin the 12 largest clusters comprising 4 or more SFPs. It can be seenthat some of these are confined to relatively short segments of a fewkilobases to 30 kb (e.g. SPCs 1, 2, 4, 5 and 7), while others span amajor part of the chromosome (e.g. SPC-3 and SPC-6). This analysisreveals patterns of SFP polymorphisms shared between yeast strains thatconsist of both locally clustered SFPs and chromosome-wide clusters, andsignifies the onset of the construction of an SPC map of the yeastgenome. A complete SPC map will entail the analysis of the yeast genomein greater depth, both in terms of the size of the strain collection andthe density of polymorphisms.

The SPC map of chromosome 1 can be used to select informative tag SFPsthat are diagnostic for each SPC identified and which can be used forgenotyping yeast strains. A subset of 12 or 19 tag SFPs can beidentified (depending on the minimum number of SFPs per cluster),representing a more than 20-fold reduction of the 406 initially observedSFPs. While the exact fold of reduction will depend on the extent oflinkage of SFPs, the example demonstrates that the methods of thepresent invention provide a straightforward approach for selecting asubset of SFPs that have the highest diagnostic value. Dedicated arrays,comprising only those oligonucleotides that interrogate the tag SFPs canthen be designed.

The present example illustrates that the methods of the presentinvention provide a rational framework for analyzing complex patterns ofgenetic variation generated on a genome-wide scale, obtained bymicroarray analysis. The example also demonstrates that the methods ofthe present invention permit the selection of tag SFPs that may beassembled on purposely designed microarrays that are useful for in vitrodiagnostic tests or genetic analysis in general.

EXAMPLE 12 SPC Analysis of Nucleotide Sequence Typing Data in Bacteria

The present example provides proof of concept that the methods of thepresent invention can be used on genetic variation data obtained withmultilocus sequence typing (MLST) of bacteria, and that the SPC mapsthus obtained are particularly useful for determining the geneticidentity of bacteria. Multilocus sequence typing (MLST) is rapidlybecoming one of the standard techniques for the characterization ofbacteria. In this technique neutral genetic variation from multiplegenomic locations is indexed by analyzing stretches of nucleotidesequence of 500 bp from loci coding for house keeping genes. Sequencedata are readily compared among laboratories and lend themselves toelectronic storage and distribution. A World Wide Web site for thestorage and exchange of data and protocols for MLST has-been established(http://mlst.zoo.ox.ac.uk). This example presents an analysis of some ofthe MLST data from a study of the gram-negative bacterium Campylobacterjejuni [Dingle et al., J. Clin. Microbiol. 39:14-23, 2001].

The aligned nucleotide sequences of the glutamine synthetase (glnA) genefrom 108 C. jejuni strains used in the present analysis were downloadedfrom the website http://mlst.zoo.ox.ac.uk. The genetic variation tableof the glnA gene comprises 107 polymorphic sites (excluding thesingletons), which were clustered with the SPC algorithm using thefollowing thresholds: C=1, C≧0.95, C≧0.90, C≧0.85 and C≧0.80. At thethreshold of C=1 and C≧0.90 the algorithm clustered a total ofrespectively 52 and 67 polymorphic sites into SPCs comprising 3 or morepolymorphic sites. The representation of FIG. 20 shows the SPC mapobtained at a threshold of C≧0.90 in which the polymorphic sites areclustered into 4 SPCs. It can be seen that the majority of polymorphicsites exhibit a simple SPC structure in that they fall into three SPCs,two of which (SPC-2 and SPC-3) are dependent on SPC-1. The fourth SPC(SPC-4) contains sites at which a third allele occurs in one sampleonly. The simple SPC pattern demonstrates that a very large number (overone hundred) of polymorphisms can be reduced to a mere three cluster tagpolymorphism to type the 108 strains at this locus. Moreover, thestraightforward dependency relationships observed provide a cleargenealogical picture of the evolution of the glnA locus.

The present example illustrates that the methods of the presentinvention provide a rational framework for analyzing complex patterns ofgenetic variation generated by multilocus sequence typing (MLST) ofbacteria. The example also demonstrates that the methods of the presentinvention permit the selection of cluster tag SNPs that may be assembledon the basis of the observed SPCs at different loci, and which areuseful for precise in vitro diagnostic of particular groups of bacteriain general.

EXAMPLE 13 Non-clustering Polymorphisms in the Surveys of GeneticDiversity in Arabidopsis thaliana

The present example illustrates that the majority of the non-clusteringpolymorphisms in a particular genomic region can be unambiguously placedin the SPC network deduced for that region. This is illustratedhereinabove for a particular human genomic region. The current examplepresents an analysis of the polymorphic sites identified in a set ofamplified fragments from chromosome 1 of Arabidopsis thaliana.

Similar to Example 6, the genomic sequences analyzed here were generatedin the NSF 2010 Project “A genomic survey of polymorphism and linkagedisequilibrium in Arabidopsis thaliana” [Bergelson J., Kreitman M., andNordborg M., http://walnut.usc.edu/2010/2010.html] and comprises, todate, 297 amplicons from chromosome 1 sequenced from 98 accessions ofArabidopsis thaliana. The sequences for this analysis were downloadedfrom the website http://walnut.usc.edu/2010/2010.html, and were alignedusing ClustalW [Thompson et al., Nucleic Acids Res. 22: 4673-4680,1994]. Using a perl script the aligned sequences were converted to agenetic variation table in which each row represents a sample and eachcolumn represents a polymorphic score. In addition to the commonbi-allelic single nucleotide substitutions, indels as well asmulti-allelic polymorphisms were observed, and were included in theanalysis. Single nucleotide indels, analogous to bi-allelic singlenucleotide substitutions, are easily represented in a single column ofthe genetic variation table. Tri-allelic SNPs are represented by twocolumns in the genetic variation table, where each entry lists the majorallele in combination with one of the minor alleles while thethird-allele-calls are replaced by blanks. Thus, the two mutationalevents that gave rise the tri-allelic marker are treated as separatepolymorphisms. Blank spaces in the genetic variation table are ignoredand frequencies of a particular allele (e.g. P_(a)) or two-sitehaplotype (e.g. P_(ab)) are calculated by simply dividing the observednumber of the allele or two-site haplotype by the total number ofsamples. Indels involving two or more nucleotides are identified by twodots at the start and the end position of the deletion. As a result ofthese indels, there is a distinction between the number of polymorphicscores (i.e. columns) in the genetic variation table and the number ofmutational events in the sequence.

The polymorphism frequency observed in the 297 amplicons from chromosome1 ranges from 0 (no mutations found) to over 25% (number of polymorphicscores over number of bases). The 5 amplicons presented here were chosenamong the most polymorphic amplicons, and are representative for thedifferent patterns of genetic variation found in Arabidopsis. The tablebelow summarizes the basic characteristics of these amplicons:chromosome position, length, total number of polymorphic scores, percentof polymorphic scores clustered and number of SPCs observed.

chromosome polymorphic scores number of amplicon position¹ length²total³ clustered⁴ SPCs⁵ A 22,903,880 540 58 43 (74%) 6 B 5,380,792 57458 47 (81%) 5 C 16,568,120 609 64 44 (69%) 13 D 22,569,092 616 61 49(80%) 7 E 13,002,329 577 89 60 (69%) 20 ¹Position of the firstnucleotide on chromosome 1 ²Total lengths of the aligned sequencesincluding insertions ³Total number of polymorphic scores ⁴Total numberof polymorphic scores that were clustered at the threshold of C = 1⁵Total numbers of SPCs containing two or more polymorphic scores

The results presented in the table and in FIG. 33 were obtained bycomputing the clustering of the polymorphic scores at the most stringentthreshold (C=1). It can be seen that most of the polymorphic sites (69%to 81%) could be clustered in a discrete number of SPCs. The panels A toD of FIG. 33 show that nearly all of the polymorphic sites(236/241)—comprising all of the SPCs as well as most of thenon-clustering polymorphisms—can unambiguously be fitted into highlybranched networks. The genetic variation tables show that only part ofthe haplotypes is defined by (major) SPCs and that a significant numberof the haplotypes is defined by non-clustering polymorphisms. This ispresumably a reflection of the fact that only short (˜600 nucleotideslong) segments have been analyzed. Some of these non-clusteringpolymorphisms may very well be found to belong to SPCs in case moreextended chromosomal regions would be sequenced. Certain othernon-clustering polymorphisms define the exterior branches of thenetworks and occur at low frequency (1% to 2%), indicating that theyrepresent recent mutations. The amplicons A to D are representative forthe type of SPC and haplotype patterns most commonly observed in theentire data set. Clearly, amplicon E is rather divergent in that itcomprises a large number of haplotypes defined by 17 independent SPCs.The network of amplicon E is essentially star shaped with few dependentSPCs.

The rare polymorphic sites (5/241) that do not fit the SPC networkstructures are also shown in FIG. 33. These represent sites whose scoresare in conflict with the proposed phylogeny of genetic variants in theamplicons. Such conflicts can have a variety of causes: sequencingerrors, recurrent mutations, historic recombination and gene conversion.Detailed analysis of the conflicting polymorphic scores suggests thatthree of these may represent sequencing errors (amplicons A, B and D),because in each case only one single or two genotype discrepancies areobserved. The first conflicting site of amplicon C is presumably arecurrent mutation of an oligo-A run, while the second site is notreadily explained.

In conclusion, the results of the analysis of genomic surveys of geneticvariation demonstrate that the SPC technology provides a crisp approachfor assessing haplotype diversity. With respect to the tag SNPs, it isworth mentioning that a broad coverage will not only require theselection of tags for the major SPCs, but also the inclusion of some ofthe non-clustering polymorphisms, more specifically those that definemajor haplotypes. As noted above, the present data sets cover very shortgenomic segments of less than 1 kb, and a non-clustering polymorphismmay be the only polymorphism of a cluster that falls in the chosenamplicon. While a short amplicon may not reveal the full geneticdiversity in a particular chromosomal region, it seems clear that theSPC analysis of the data at hand allows the identification of the mostinformative polymorphisms for genetic association analysis.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A computer-implemented method of producing a map of a genomic regionof interest said map on a computer comprising one or more sequencepolymorphism clusters (SPCs), wherein each SPC comprises a subset ofpolymorphisms from said genomic region wherein said polymorphisms ofsaid subset co-segregates with each other polymorphism of said subset;and wherein said map further comprises non-co-segregating polymorphismsthat are associated with the map, wherein said non-co-segregatingpolymorphisms are such that they do not co-segregate with any otherpolymorphism but do co-segregate with at least one SPO, said methodcomprising the steps of: a. obtaining the nucleic acid sequence of saidgenomic region of interest from a plurality of subjects; b. identifyingon a computer a plurality of polymorphisms in said nucleic acidsequences; c. identifying on a computer one or more SPCs, wherein eachSPC comprises a subset of polymorphisms from said nucleic acid sequencewherein each of said polymorphisms of said subset co-segregates witheach other polymorphism of said subset; and d. identifying on a computernon-co-segregating polymporphisms that do not co-segregate with anyother polymorphism but do cosegregate with at least one SPC e.outputting from said computer the SPCs identified in step (c) and thenon-co-segregating polymorphisms identified in step (d) into a userreadable format that selects a genotype or genetic marker for a trait orphenotype.
 2. The method of claim 1, wherein each said polymorphism ofsaid subset co-segregates with each other polymorphism of said subsetaccording to a percentage co-segregation of the minor alleles of saidpolymorphisms of between 75% and
 100. 3. The method of claim 1, whereinthe co-segregation of each said polymorphism of said subset with eachother polymorphism of said subset is calculated according to a parameterselected from the group consisting of a pairwise C value, C* value, ar.sup.2 linkage disequilibrium value, a Δ linkage disequilibrium value,a δ linkage disequilibrium value, and a d linkage disequilibrium value.4. The method of claim 1, wherein said parameter is a pairwise C valueof from 0.75 to
 1. 5. The method of claim 1, wherein said identifyingone or more SPCs comprises identifying each polymorphism of said subsetthat co-segregates with each other polymorphism of said subset accordingto a percentage co-segregation of the minor alleles of saidpolymorphisms of between 75% and 100%.
 6. The method of claim 1, whereinsaid identifying one or more SPCs comprises multiple rounds ofco-segregation analysis.
 7. The method of claim 1, wherein eachsuccessive round of co-segregation analysis is performed at a decreasingpercentage co-segregation from 100% co-segregation to 75%co-segregation.
 8. The method of claim 1, wherein the co-segregation ofeach said polymorphism of said subset with each other polymorphism ofsaid subset is calculated according to a parameter selected from thegroup consisting of a pairwise C value, C* value, a r² linkagedisequilibrium value, a Δ linkage disequilibrium value, a δ linkagedisequilibrium value, and a d linkage disequilibrium value.
 9. Themethod of claim 8, wherein said parameter is a pairwise C value of from0.75 to
 1. 10. An article comprising a machine-accessible medium havingstored thereon instructions that, when executed by a machine, cause themachine to: obtain a nucleic acid sequence of a genomic region ofinterest from a plurality of subjects; identify a plurality ofpolymorphisms in said nucleic acid sequence; identify one or more SPCs,wherein each SPO comprises a subset of polymorphisms from said nucleicacid sequence wherein each said polymorphisms of said subsetco-segregates with each other polymorphism of said subset; identifynon-cosegregating polymorphisms that do not coincide with any otherpolymorphism but do coincide with at least one SPC, and output theidentified SPCs and non-cosegregating polymorphisms into a user readableformat that selects a genotype or genetic marker for a trait orphenotype.